WO2019047170A1

WO2019047170A1 - Pseudo base station identification method and terminal

Info

Publication number: WO2019047170A1
Application number: PCT/CN2017/101088
Authority: WO
Inventors: 龙水平; 董辰; 衣强; 李重锦
Original assignee: 华为技术有限公司
Priority date: 2017-09-08
Filing date: 2017-09-08
Publication date: 2019-03-14
Also published as: CN110178395A

Abstract

Provided are a pseudo base station identification method and a related device. The method comprises: a terminal selecting a target cell; the terminal running a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence, wherein the confidence is used for indicating the degree of confidence that the base station of the target cell is a pseudo base station, and the pseudo base station identification algorithm is generated by means of training of a machine learning algorithm; and when the confidence is greater than or equal to a first confidence threshold, the terminal determining that the base station of the target cell is a pseudo base station. The pseudo base station identification algorithm is generated by means of training of a machine learning algorithm, so that the pseudo base station identification algorithm can be run according to the feature data of the target cell to obtain a confidence, and the authenticity of the base station of the target cell is identified by means of the confidence. The pseudo base station identification algorithm is more complex than the feature data matching algorithm, and it is easier to expand new feature data, so that the identification success rate for a pseudo base station can be improved.

Description

Pseudo base station identification method and terminal

Technical field

The present invention relates to the field of communications, and in particular, to a pseudo base station identification method and terminal.

Background technique

A pseudo-base station controlled by an illegal organization or individual, independent of the public mobile network, by a base station disguised as a mobile communication carrier (for example, broadcasting a public land mobile network ID (PLMN ID) of a mobile operator) The terminal (or mobile terminal, mobile station, mobile phone, user equipment, etc.) can be tricked into initiating a network registration or location update request, thereby extracting information of the terminal, for example, an International Mobile Subscriber Identification Number (IMSI). ), Temporary Mobile Subscriber Identity (TMSI), or International Mobile Equipment Identity (IMEI), can also transfer information with the terminal, such as sending fraudulent text messages, malicious network links, or Harassing short messages, etc., thereby jeopardizing the user. The pseudo base station can perform network registration spoofing on the terminal, and can forge any number to send a short message to the terminal, that is, the pseudo base station is not only a base station, but also has a certain mobile network core network function. The wireless signal can form one or more cellular cell signal coverage, and can change system broadcast parameters such as cell identity, and when the terminal performs a standard cell selection (or search network) process or a cell reselection process, The selected target cell may be a cell formed by a certain pseudo base station (referred to as a pseudo base station cell or a pseudo cell). After the terminal selects the target cell, network registration, camping, service request, or location area update may be performed.

The existing method of the terminal anti-counterfeiting base station (or anti-pseudo base station) generally adopts feature data matching or intercepting the problem text message. For example, in the anti-spyware base station method for feature data matching (or feature parameter matching), specifically, based on manual statistics and experience of the pseudo-base station cell system broadcast parameter sample data, a plurality of cell system broadcast parameters are selected, and the pseudo base station is determined. The common value range of the broadcast parameters of the system to the system (often different from the true base station cell, these parameters are therefore called feature data), if several system broadcast parameters of the target cell to be evaluated match the feature data of the pseudo base station cell The common value range determines (or determines) that the base station of the target cell is a pseudo base station, or the target cell is a pseudo cell. The common value range of the system broadcast parameters of the foregoing pseudo base station cell, for example, the LAC values 0, 65535, the minimum receiving level, the maximum power level, and the cell reselection offset (CRO) are often set to 0 and so on. However, the system broadcast parameters of the new pseudo base station cell are very concealed. For example, it may duplicate the system broadcast parameters of the surrounding real base station cells and make certain modifications, which may invalidate the anti-spyware base station method for matching the feature data. The problem message interception is to identify the fraudulent message, the malicious network link or the harassment message through the cloud message big data analysis, and extract the feature information of the problem message, and then send the problem message feature information to the problem message interception application on the terminal, and the interception application pair The short message received by the terminal is detected. If the message characteristic information of the problem is met, the short message is placed in the intercepted state, and the blocked short message is not seen in the normal short message user interface. The problem message may come from a legitimate carrier mobile network, or a pseudo base station. The problem message interception only has the effect of appropriately reducing the harm of the pseudo base station, but the pseudo base station cannot be identified, and the terminal cannot be prevented from being deceived by the pseudo base station. Therefore, the existing pseudo base station identification method has a problem that the pseudo base station identification rate is low.

Summary of the invention

The embodiment of the invention provides a pseudo base station identification method and a terminal, which are used to improve the recognition success rate of the pseudo base station.

In a first aspect, a method for identifying a pseudo base station is provided. The method includes: the terminal selects a target cell, and after acquiring the feature data of the target cell, the terminal runs a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level. The confidence level is used to indicate that the base station of the target cell is a trusted base station, and the pseudo base station identification algorithm is generated by the machine learning algorithm training. The target cell can be authenticated according to the confidence. When the confidence is greater than or equal to the first confidence threshold, the terminal determines that the base station of the target cell is a pseudo base station, that is, determines that the target cell is a pseudo cell. After determining that the base station of the target cell is a pseudo base station, the terminal may perform related operations to avoid the harm caused by the pseudo base station. The pseudo base station identification algorithm is generated by the machine learning algorithm using a large number of sample data of the true and pseudo base station cells (referred to as true base station data and pseudo base station data), the recognition performance is high, and the training can be continuously trained to quickly follow the evolution of the pseudo base station technology. Therefore, the recognition success rate of the pseudo base station can be improved.

In a first possible implementation manner of the first aspect, after the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and after obtaining the confidence, the method further includes: when the confidence is less than the first confidence threshold and the confidence is When the terminal detects that the target cell meets the first preset condition, the terminal determines that the base station of the target cell is a pseudo base station. The second confidence threshold is smaller than the first confidence threshold. If the confidence of the target cell is between the first confidence threshold and the second confidence threshold, the base station of the target cell is a suspected pseudo base station, and the first preset condition is The base station is behavior information generated by the cell of the pseudo base station. Therefore, if the terminal further detects that the target cell meets the first preset condition, the terminal determines that the base station of the target cell is a pseudo base station. In this way, the authenticity identification of the target cell by the confidence degree is described from the probability aspect. When the confidence level is in the confidence interval to which the suspected pseudo base station belongs, the terminal can recognize that the confidence is less than the first confidence condition. A confidence threshold but the base station is the target cell of the pseudo base station.

In a second possible implementation manner of the first aspect, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal Determining that the base station of the target cell is a pseudo base station, including: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal saves the target cell Characteristic data, the feature data of the target cell is identified as pseudo base station data. In this case, the pseudo base station identification algorithm is not ideal for identifying the target cell. After the terminal saves the feature data of the target cell, the terminal may send the feature data of the target cell to a device such as a cloud server, so that the cloud server determines the The feature data of the target cell is pseudo base station data, and the pseudo base station identification algorithm is further trained using the feature data of the target cell that is the pseudo base station data.

In a third possible implementation manner of the first aspect, the terminal saves the feature data of the target cell, where the terminal saves the feature data of the target cell when the pseudo base station data save function is enabled, or when the target cell satisfies the pseudo base station data. When the rule is saved, the terminal saves the feature data of the target cell; or, when the pseudo base station data save function is enabled, and the target cell satisfies the pseudo base station data storage rule, the terminal saves the feature data of the target cell. In this way, the feature data of the target cell identified as the pseudo base station data can be flexibly saved on the terminal, the control of the terminal data storage overhead is facilitated, and the feature data of the pseudo base station cell that meets the requirements can be selected.

In a fourth possible implementation manner of the first aspect, the first preset condition includes at least one condition that the terminal intercepts the problem message sent by the target cell; the terminal is rejected when the location area update request is initiated to the target cell; When the service request is initiated to the target cell, the terminal is rejected; the terminal loses the target cell signal within a preset time; or the location area code LAC of the target cell changes. These conditions are behavior information generated by the base station as a cell of the pseudo base station.

In a fifth possible implementation manner of the first aspect, after the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and after obtaining the confidence, the method further includes: when the confidence is less than or equal to the fourth confidence threshold The terminal determines that the base station of the target cell is a real base station, so that the terminal performs a preset operation by using the target cell, where the preset operation includes any one of network registration, location area update, cell camping, and originating service request. After the terminal identifies that the base station of the target cell is a true base station, the terminal performs a preset operation through the target cell, so that the terminal is in a more secure environment.

In a sixth possible implementation manner of the first aspect, after the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and after obtaining the confidence, the method further includes: when the confidence is greater than the fourth confidence threshold and the confidence is When the terminal detects that the target cell meets the second preset condition, the terminal determines that the base station of the target cell is a true base station. The third confidence threshold is greater than the fourth confidence threshold. If the confidence of the target cell is between the fourth confidence threshold and the third confidence threshold, indicating that the base station of the target cell is a suspected true base station, and the second preset condition is that the base station is behavior information generated by the cell of the true base station, If the terminal further detects that the target cell meets the second preset condition, the terminal determines that the base station of the target cell is a true base station. In this way, since the authenticity of the target cell is determined from the probability aspect, when the confidence level is in the confidence interval to which the suspected true base station belongs, the terminal can recognize that the confidence is greater than the second confidence condition. A cell with a four confidence threshold but the base station is a true base station.

In a seventh possible implementation manner of the first aspect, when the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, the terminal Determining that the base station of the target cell is a true base station, including: when the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, the terminal saves the target cell Characteristic data, the feature data of the target cell is identified as true base station data. In this case, the pseudo base station identification algorithm is not ideal for identifying the target cell. After the terminal saves the feature data of the target cell, the terminal may send the feature data of the target cell to the cloud server or the like, so that the cloud server determines the The feature data of the target cell is true base station data, and the pseudo base station identification algorithm is further trained using the feature data of the target cell that is the true base station data.

In an eighth possible implementation manner of the first aspect, the terminal saves the feature data of the target cell, where the terminal saves the feature data of the target cell when the true base station data save function is enabled, or saves the target cell to the true base station data. When the rule is used, the terminal saves the feature data of the target cell; or, when the true base station data save function is enabled, and the target cell satisfies the true base station data storage rule, the terminal saves the feature data of the target cell. In this way, the feature data of the target cell identified as the true base station data can be flexibly saved on the terminal, the control of the terminal data storage overhead is facilitated, and the feature data of the true base station cell that meets the requirements can be selected.

In a ninth possible implementation manner of the first aspect, the second preset condition includes at least one condition that the terminal establishes a call or a data service in the target cell; the terminal completes the authentication through the target cell and enters an encryption security mode; or, the terminal The handover is completed in the target cell. These conditions are behavior information generated by a cell in which the base station is a true base station.

In a tenth possible implementation manner of the first aspect, the target cell is a global mobile communication system GSM cell. Since the current pseudo base station is mostly a GSM base station, the present method is implemented for a scenario in which the target cell is a GSM cell, and is effective for most pseudo base stations.

In an eleventh possible implementation manner of the first aspect, the terminal runs the pseudo base station according to the feature data of the target cell. The identification algorithm obtains a confidence level, including: when the terminal detects that the LAC of the target cell is different from the currently saved LAC, the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and obtains a confidence level. This can reduce power consumption and improve recognition efficiency.

In a twelfth possible implementation manner of the first aspect, after the terminal determines that the base station of the target cell is a pseudo base station, the method further includes: the terminal prohibiting the target cell from being selected again within the preset duration. In this way, by prohibiting the selection of the target cell again within the preset duration, the interference of the pseudo base station cell to the terminal can be reduced, and the influence of the terminal on the selection of the impersonated true base station cell can be reduced.

In a thirteenth possible implementation manner of the first aspect, the feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information. Any of these kinds of information may also include a plurality of specific cell information.

In a second aspect, a terminal is provided, the terminal having the function of implementing the terminal in the method of the first aspect described above. This function can be implemented in hardware or in hardware by executing the corresponding software. The hardware or software includes one or more modules corresponding to the functions described above.

In a third aspect, a terminal is provided that includes a processor and a memory. The processor may be configured to support a terminal to perform a corresponding function in the method described in the foregoing first aspect, for example, the processor is configured to: select a target cell; and run a pseudo base station identification algorithm according to feature data of the target cell to obtain a confidence The confidence level is used to indicate that the base station of the target cell is a trusted level of the pseudo base station, and the pseudo base station identification algorithm is generated by the machine learning algorithm training; when the confidence level is greater than or equal to the first confidence threshold, the base station of the target cell is determined to be a pseudo Base station.

In a fourth aspect, a chip arrangement is provided, the chip arrangement comprising a processing unit for performing the method of the first aspect described above.

In a fifth aspect, a chip arrangement is provided, the chip arrangement comprising a processor and a memory. The memory includes instructions that are executed by the processor to perform the method of the first aspect described above.

In a sixth aspect, a chip system is provided, the chip system comprising a processor for supporting a terminal to implement the functions involved in the first aspect described above, such as transmitting or processing data and/or information involved in the above method. In a possible design, the chip system further includes a memory for storing necessary program instructions and data of the network device. The chip system can be composed of chips, and can also include chips and other discrete devices.

In a seventh aspect, a computer program is provided comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect described above.

In an eighth aspect, a computer readable storage medium is provided, the computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method of the first aspect described above.

In a ninth aspect, a computer program product comprising instructions for causing a computer to perform the method of the first aspect described above when the instructions are run on a computer.

In the embodiment of the present invention, the terminal can obtain the confidence according to the feature data of the target cell by running the pseudo base station identification algorithm based on the machine learning, and determine whether the base station of the target cell is a pseudo base station according to the confidence level. The pseudo base station identification algorithm generated by a large number of feature data training improves the recognition rate of the terminal to the pseudo base station. Moreover, the terminal may send the feature data of the suspected base station to the cloud server, and the cloud server updates the pseudo base station identification algorithm, thereby continuously following the technical evolution of the pseudo base station, and continuously improving the recognition rate of the pseudo base station.

DRAWINGS

FIG. 1 is a schematic diagram of a scenario related to a pseudo base station identification method according to an embodiment of the present disclosure;

Figure 1b is another schematic view of the scene shown in Figure 1a;

2 is a flowchart of a method for identifying a pseudo base station according to an embodiment of the present invention;

3 is a schematic diagram of a confidence threshold relationship involved in the pseudo base station identification method shown in FIG. 2;

4 is a flowchart of a method for identifying a pseudo base station according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a scenario structure involved in the pseudo base station identification method in the embodiment shown in FIG. 4; FIG.

FIG. 6 is a schematic structural diagram of a terminal according to an embodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of hardware of a terminal according to an embodiment of the present invention.

Detailed ways

In order to facilitate the understanding of the various embodiments provided by the present invention, some terms used in the embodiments of the present invention will be explained below, and the following embodiments can be explained with reference to the following terms.

1. The pseudo base station mentioned in the present application is a base station controlled by an illegal organization or an individual, and the pseudo base station is spoofed as a base station of a mobile communication carrier, for example, broadcasting a PLMN of a mobile operator, independently of the public mobile network. ID, can trick a terminal (or mobile terminal, mobile station, mobile phone, user equipment, etc.) to initiate a network registration or location update request, thereby extracting information of the terminal, such as IMSI, TMSI or IMEI, and can also perform information with the terminal. Delivering, etc., for example, sending a scam message, a malicious network link, or harassing a message to the terminal. The pseudo base station can perform network registration spoofing on the terminal, and can forge any number to send a short message to the terminal. Therefore, the pseudo base station is not only a base station, but also has a certain mobile network core network function. The pseudo base station transmits a strong wireless signal, can form one or more cellular cell signal coverage, and can change system broadcast parameters such as cell identifier of each cell.

The basic principles of the operation of various types of pseudo base stations are similar. The principle is generally: the pseudo base station masquerades as a legal base station, and then transmits a strong cell signal, attracts the terminal to stay and register, obtains the IMSI of the terminal, and then sends the fake SMS to The terminal can record the terminal that has sent the fake SMS, avoids repeated transmission, and kicks the terminal out of the cell in time.

2. Machine Learning (ML) is a multi-disciplinary subject involving many disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. Specializing in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance.

Machine learning algorithms can be, for example, decision trees, random forest algorithms, logistic regression, SVM (Support Vector Machine), Naive Bayes, K nearest neighbor algorithm, K-means algorithm, Adaboost algorithm, neural network and Markov Wait.

Machine learning algorithms that use a large number of labeled data samples for training or learning can produce model instances or function instances that have the ability to auto-tag new data samples.

3. Location area code (LAC). In order to determine the location of the mobile station, the coverage area of each Public Land Mobile Network (PLMN) is divided into a plurality of location areas, and the LAC can be used for identification. Different location areas. A location area may contain one or more cells. LAC broadcasts a message in each cell Sent in the system message on the track. For example, the base station broadcasts the cell frequency and the location area identity (LAI), and the information of the adjacent cell broadcast control channel (BCCH) frequency to the mobile phone through the BCCH. Among them, LAI includes LAC.

4. Principle of LTE RRC (Long Term Evolution Radio Resource Control) redirection attack: LTE pseudo base station attracts LTE terminal to attach; after receiving the attachment request from the terminal and before the security process starts, directly The non-access stratum (NAS) message is rejected, and the RRC ConnectionRelease message is sent. The message carries the redirectedCarrierInfo message, indicating that the second generation mobile phone communication technology is transmitted to the 2G (2-Generation wireless telephone technology). Specification) Network and Frequency Point (ARFCN), which is usually a pre-configured pseudo base station, which is convenient for attackers to carry out the next attack.

5. The process of selecting a cell according to criterion 1 (Criteria 1, C1) or criterion 2 (Criteria 2, C2):

1) When the terminal searches for the network, the terminal selects a cell with C1>0 and the largest C1;

2) When the terminal performs cell reselection, it involves the C2 criterion (or C2 algorithm). Cell reselection will be initiated if one of the following conditions is met;

(A) C1 is always less than 0 during the 5 second calculation period;

(B) In the calculation period of 5 seconds, if the current cell and the neighboring cell have the same LAC, when the C2 of the neighboring cell is always greater than the C2 value of the serving cell, cell reselection may occur; if the current cell and the neighboring cell have different LACs The C1 of the neighboring cell is always greater than the sum of the C1 of the serving cell and the Cell Reselection Hysteresis (CRH), and cell reselection may occur.

Including the above two conditions, if cell reselection has occurred within the previous 15 seconds, the C2 of the new candidate cell must always be 5 dB higher than the current serving cell in the 5 second calculation period to initiate cell reselection.

Where C1 = terminal reception average level - minimum level that allows terminal access; C2 = C1 + cell reselection offset = terminal reception average level + (cell reselection offset - minimum power allowed for terminal access) level).

FIG. 1 is a schematic diagram of a scenario involved in a method for identifying a pseudo base station according to an embodiment of the present invention. As shown in FIG. 1a, the scenario involved in the pseudo base station identification method includes a base station 101 and a terminal 102, and the terminal 102 can be communicatively coupled to the base station 101.

The terminal 102 can include, but is not limited to, a mobile phone, a tablet, a Personal Digital Assistant (PDA), a Point of Sales (POS), a car computer, and the like.

The base station 101 can broadcast signals to form one or more cells, as shown in Figure 1b.

In some examples, base station 101 can be a true base station and a true base station forms a true base station cell. The real base station is a legal base station of the operator mobile network, and the terminal 102 can access the operator mobile network, change the camping cell or the handover cell through the real base station cell, and perform call and data services through the real base station cell.

In other examples of the present invention, the base station 101 may also be a pseudo base station, and the definition of the pseudo base station may refer to the above description. When the base station 101 is a pseudo base station, the pseudo base station 101 transmits a signal to form a pseudo base station cell. The terminal 102 may select the pseudo base station cell when performing a standard cell selection (or network) process or a cell reselection procedure. After the terminal selects the pseudo base station cell, operations such as network registration, camping, service request, or location area update may be performed.

Since the pseudo base station 101 can extract information such as the IMSI, TMSI, and IMEI of the terminal 102, it can also be associated with the terminal 102. For example, the pseudo base station 101 transmits a fraudulent short message, a malicious network link, or a harassment message to the terminal 102, thereby jeopardizing the user's use of the terminal 102. The pseudo base station 101 also makes the terminal 102 not actually connected to the normal mobile network, and the normal call and data service of the terminal cannot be performed.

The pseudo base station identification method and the terminal according to the embodiment of the present invention are described in detail below in conjunction with the above.

FIG. 2 is a flowchart of a method for identifying a pseudo base station according to an embodiment of the present invention. The method is applicable to a terminal in the scenario shown in FIG. 1a. Referring to FIG. 2, the method of the embodiment of the present invention includes:

Step 201: The terminal selects a target cell.

Step 202: The terminal runs a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level.

Step 203: When the confidence is greater than or equal to the first confidence threshold, the terminal determines that the base station of the target cell is a pseudo base station.

In step 201, the terminal needs to perform target cell selection in order to access the mobile network, change the camped cell or switch the cell. The base station performs signal broadcast to form a cell signal coverage, and the terminal may perform target cell selection according to the acquired broadcast signal, and the terminal may select the pseudo base station cell.

Specifically, the base station broadcasts a signal to the terminal through a BCH (Broadcast Channel), and the signal may include, for example, a frequency correction signal, a synchronization signal, a local cell frequency and an LAC, and information such as a neighboring cell BCCH frequency. The terminal can then select the target cell based on the C1 or C2 criteria.

The terminal selects the target cell, acquires a broadcast signal of the target cell, and obtains feature data of the target cell from the broadcast signal, where the feature data is used to calculate or evaluate the degree of trust of the target cell as a pseudo base station.

In the embodiment of the present invention, the target cell may be a plurality of types of cells, for example, the target cell may be a GSM (Global System for Mobile communication) cell, and the base station is a GSM base station; or, the target cell is 3G ( 3rd-Generation, 3rd generation mobile communication technology), the base station is a 3G base station; or the target cell is 4th (the 4th Generation mobile communication) network cell, and its base station is LTE (Long Term) Evolution, Long Term Evolution) / 4G base station. The current pseudo base station is mostly a GSM base station. For convenience of description, the present application uses the target cell as a GSM cell as an example for description.

Step 201 can be implemented in various scenarios. A specific implementation of step 201 will be described below. In these examples, the terminal may select a pseudo base station cell.

Example 1: A terminal is an idle state terminal camping on a GSM network, and the terminal selects a target cell according to a cell reselection procedure to change a camped cell (also called a serving cell).

For example, if the C2 of the target cell is greater than the serving cell C2 for 5 consecutive seconds, and the target cell has the same LAC as the serving cell, the terminal reselects the target cell as the (new) serving cell. In the cell reselection procedure, the terminal may select a pseudo base station cell.

Example 2: When the terminal changes from the resident 4G/3G network to the GSM network, the terminal selects the target cell according to the cell selection process.

A terminal residing on a 4G/3G network may fall back to the GSM network because the current network signal is degraded. At this time, the GSM network cell selection of the terminal may also be selected to the pseudo base station cell. In addition, some pseudo base stations may transmit 4G/3G interference signals, resulting in poor signal quality (such as carrier-to-interference ratio) of artificial 4G/3G network cells, forcing the terminal to fall back to The GSM network then attracts the terminal to camp through the GSM pseudo base station cell.

Example 3: When the terminal is powered on, the target cell is selected through the cell selection process.

When the terminal is powered on, the terminal performs GSM network cell selection for network attachment (ie, network registration). At this time, the terminal may also select a pseudo base station cell.

The scenario of the three types of target cells is the cell reselection or the cell selection behavior of the terminal in the idle state. In the embodiment of the present invention, the terminal may also be in the connected state (that is, the terminal is connected to the serving cell) to perform the target cell. s Choice. The specific situation is as follows:

Example 4: The terminal selects the target cell through redirection from the 4G/3G network.

The connected terminal is triggered by the redirection command of the 4G/3G network, and may fall back to the GSM network for cell selection. For example, the 4G/3G network instructs the terminal to redirect to the 2G network and indicates the 2G neighbor cell frequency point information, and the terminal captures the 2G neighbor cell signal and initiates a service request (such as answering an incoming call) through the 2G network.

In the redirection scenario, if the terminal selects a GSM pseudo base station cell, it will be rejected by the pseudo base station cell when the service request is initiated (because the pseudo base station generally cannot establish the corresponding service), although the probability of occurrence of such a scenario is not large, However, once it occurs, it will cause a large interference to the user's use of the terminal. In addition, the new 4G/3G pseudo base station may also first attract the terminal to camp on its cell, and then deny the terminal to fall back to the GSM network through the redirect, and then attract the terminal to camp through the GSM pseudo base station cell.

Example 5: When the terminal switches the GSM cell in the service state, the terminal searches for the target cell specified by the network and selects the target cell.

For example, the GSM serving cell handover may occur in the call state or the data service state of the terminal, and the network sends the handover target cell information to the terminal, and the terminal searches for the target cell and selects the target cell. In this case, the terminal may select a pseudo base station cell, causing the handover to fail to complete normally, causing problems such as dropped calls or data service interruption.

In step 202, the confidence level is used to indicate that the base station of the target cell is the trustworthiness of the pseudo base station, and the pseudo base station identification algorithm is generated by the machine learning algorithm training.

After the terminal selects the target cell, the feature data of the target cell may be obtained from the signal broadcast by the target cell, where the signal may be a signal that the target cell broadcasts through the BCH. In other words, the feature data of the target cell can be extracted from the signal broadcast by the target cell, and the feature data is used to calculate or evaluate the degree of trust of the target cell as a pseudo base station.

The feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information, as follows:

The feature data includes any one of cell selection and cell reselection information, networking information, service function information, area information, or time information.

Alternatively, the feature data includes any two of cell selection and cell reselection information, networking information, service function information, area information, and time information. For example, the feature data includes cell selection and cell reselection information, and networking information. .

Alternatively, the feature data includes any three of cell selection and cell reselection information, networking information, service function information, area information, and time information. For example, the feature data includes cell selection and cell reselection information, networking information, And business function information.

Alternatively, the feature data includes any four of cell selection and cell reselection information, networking information, service function information, area information, and time information. For example, the feature data includes cell selection and cell reselection information, networking information, Business function information and geographic information.

Alternatively, the feature data includes cell selection and cell reselection information, networking information, service function information, area information, and time information.

It can be understood that any one of cell selection and cell reselection information, networking information, service function information, area information, and time information may further include multiple specific cell information. A detailed description of these specific cell information is as follows:

1) Cell selection and reselection information, including but not limited to: PLMN-ID, RXLEV-ACCESS-MIN (minimum access level), MS-TXPWR-MAX-CCH (terminal maximum transmit power), MAX-RETRANS (maximum Retransmission number), TX-INTEGER (number of transmission slots); reselection parameter indication (PI), cell reselection offset (CRO), temporary offset (TO), penalty Time (penalty time, PT), cell reselection hysteresis (CRH), current and previous location area code (LAC), base station identity code (BSIC), cell identity (cell identity, CI). The cell selection and reselection related information may be one or any combination of such information.

2) Network information, including but not limited to: CCCH-CONF (Common Control Channel Configuration), BS-AG-BLKS-RES (Access Allowed Block Number), BA-PA-MFRMS (Paging Channel Multiframe) , T3212 (periodic update duration), with or without neighboring cells (BA1/BA2). The networking related information may be one or any combination of the information.

3) Service function information, including but not limited to: whether to support GPRS information, whether to support emergency call (EC) information.

4) Geographical information, including but not limited to: current location information of the terminal (such as geographic location latitude and longitude information).

5) Time information, including but not limited to: current time information of the terminal (such as month, day, week, hour, minute).

The above BA1 and BA2 are explained as follows:

BA list (BA list), that is, BCCH allocation information, is used in cell selection, cell reselection, or cell measurement. The BA list is divided into an Idle list (BA1) and an Active list (BA2). Idle list: The list information is sent on the BCCH through the system information block type 2, and is used for cell selection and reselection in the idle state of the terminal, and up to 32 frequency points are set. Active list: The list information is sent on the BCCH through the system information block type 5. The frequency point is the frequency of the temporary cell that should be measured when the terminal is in the call state. When the cell is switched, there are a total of 32.

A pseudo base station identification algorithm is pre-configured on the terminal. After the terminal acquires the feature data of the target cell, in order to identify whether the target cell is a pseudo base station cell, the terminal runs a pseudo base station identification algorithm according to the feature data of the target cell, and obtains a confidence level. The degree is used to indicate the degree of trust of the base station of the target cell as a pseudo base station.

Specifically, the terminal may provide the feature data of the target cell to the pseudo base station identification algorithm, and the pseudo base station identification algorithm uses the feature data to perform calculation, and outputs a confidence. The confidence level may be in the range of 0 to 1. The greater the value of the confidence, the greater the probability that the base station of the target cell is a pseudo base station, and the smaller the value of the confidence, the base station indicating the target cell is The probability of a true base station is greater. In other words, the confidence is a parameter describing the probability event.

For example, the machine learning algorithm is taken as an example of the SVM algorithm. At this time, the pseudo base station identification algorithm is generated by the SVM algorithm training, and the pseudo base station identification algorithm is specifically an SVM model instance. Since the SVM algorithm is a binary classification algorithm, the pseudo base station identification algorithm can classify the base stations of the cell into a true base station and a pseudo base station, and output a confidence level as a result of the above classification. Specifically, the terminal selects cell selection and cell reselection information, networking information, service function information, area information, and time information of the target cell. One or more kinds of information are provided for use by the pseudo base station identification algorithm, for example, the terminal selects TX-INTEGER (number of transmission slots), cell reselection offset (CRO), penalty time in cell selection and reselection information ( PT), the periodic update duration in the networking information, and the current location information of the terminal or the target cell in the regional information are provided to the pseudo base station identification algorithm for calculation, and the pseudo base station identification algorithm uses the feature data to perform calculation, and obtains a confidence. The degree is 0.95, and the confidence level of 0.95 indicates that the probability that the base station of the target cell is a pseudo base station is 95%.

In the embodiment of the present invention, the pseudo base station identification algorithm may be generated by the sample data of a large number of cells, and the sample data of the cell includes the sample data of the pseudo base station cell and the real base station cell. Sample data. It can be understood that the sample data of the cell can be used to represent the characteristics of the cell.

Since the pseudo base station identification algorithm is generated by the machine learning algorithm training, the pseudo base station identification algorithm has the performance advantages of the machine learning algorithm, and the description of the machine learning algorithm can refer to the contents of the machine learning part above. The machine learning algorithm may be a clustering algorithm or a classification algorithm as described above, such as K-means, k-nearest neighbor, decision tree, logistic regression, SVM, or Bayesian algorithm, or other existing machine learning algorithms. It will not be repeated here.

In an example, the step 202 may include: when the terminal detects that the LAC of the target cell is different from the currently saved LAC, the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level.

Specifically, the terminal compares the LAC of the target cell with the currently stored LAC. If the LAC of the target cell is different from the currently stored LAC, the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level. If the LAC of the target cell is the same as the currently stored LAC, indicating that the base station of the target cell is a true base station, the terminal does not run the pseudo base station identification algorithm. The currently saved LAC is the LAC of the serving cell, or the currently saved LAC may be the LAC of the current location of the GSM network obtained by the terminal through the joint attach/join location update. The serving cell may be a cell in which the idle state terminal currently camps, or a cell to which the terminal currently has a connection, or a cell in which the terminal currently performs a service (for example, a call or a data service). In the case of joint attach/join location update, the terminal camping on the 4G/3G network can obtain the LAC of the current location of the GSM network through the 4G/3G network cell.

According to the rule of the pseudo base station, if the LAC of the target cell is the same as the currently stored LAC, it indicates that the base station of the target cell is a high probability event, and it is redundant or uneconomical to perform the pseudo base station identification algorithm at this time. Therefore, when the terminal detects that the LAC of the target cell is different from the currently saved LAC, the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell. In this way, resources such as power and computing performance of the terminal are saved, power consumption is reduced, and the networking speed and recognition efficiency of the terminal are improved.

It can be understood that there may be multiple timings for running the pseudo base station identification algorithm, which is not limited in this application. In an example, since the pseudo base station identification algorithm has a certain amount of calculation and power consumption, the timing of running the pseudo base station identification algorithm can be placed after the cell reselection (or cell selection process) is completed, so that the function of the terminal can be reduced. Consumption. In other examples, the pseudo base station identification algorithm may also be run at other times. In some examples, the pseudo base station identification algorithm may operate during the selection of the target cell, for example, may be run before the C2 algorithm (or C2 criterion). The pseudo base station identification algorithm can add the confidence of the output of the pseudo base station identification algorithm to the C2 algorithm to perform identification of the pseudo base station, so that the terminal does not reselect to the pseudo base station cell.

In step 203, the terminal determines whether the confidence obtained in step 202 is greater than or equal to the first confidence threshold. When the confidence level is greater than or equal to the first confidence threshold, the base station indicating the target cell is a large probability event, and the terminal may determine that the base station of the target cell is a pseudo base station. The terminal can discard the target cell and will not further camp The target cell initiates a service request or the like through the same, and continues cell selection or cell reselection to select other target cells, or ends cell reselection. The target cell is a pseudo base station cell, and the terminal may further perform operations on the pseudo base station cell, for example, prohibiting to select the cell with the cell identifier as the pseudo base station cell identity again, and saving the pseudo base station cell within a preset duration (for example, 10 seconds). The feature data is used to report to the cloud server or send a prompt message to the user of the terminal (for example, prompting the user that the pseudo base station is successfully intercepted).

When the confidence is less than the first confidence threshold, the terminal can perform other operations. The other operations may be, for example, determining that the base station of the target cell is a true base station according to the confidence level, or other situations of the other operations may also refer to "1.1, recognizing the suspected pseudo base station again" and "1.2, recognizing the suspected true base station again". The content described in .

The first confidence threshold may be a preset confidence threshold, and the first confidence threshold is used to divide whether the base station of the target cell is a pseudo base station. Through the reasonable setting of the first confidence threshold, the pseudo base station identification algorithm can meet the performance requirement requirements of the designer, and the performance indicator includes a pseudo base station identification rate or a pseudo base station false alarm rate, wherein the pseudo base station false alarm rate indicates that the true The probability that the base station cell is erroneously identified as a pseudo base station cell. The first confidence threshold may be set according to at least one of a test result of the pseudo base station identification algorithm, a performance indicator of the pseudo base station identification algorithm (eg, a pseudo base station identification rate, a pseudo base station false alarm rate), and the like. For example, after the machine learning algorithm training generates the pseudo base station identification algorithm, the performance test of the algorithm may be performed. First, the first confidence threshold (such as 0.95) is set, and then the pseudo base station identification rate of the pseudo base station cell test sample set is obtained (for example, 0.90), and the pseudo base station false alarm rate (such as 0.001) of the algorithm for the true base station test sample set, by adjusting the first confidence threshold, different algorithm performance indicators can be obtained. When the performance index of the algorithm meets the requirements of the designer, the algorithm can be transplanted, and the pseudo base station identification algorithm is transplanted to each terminal. It can be understood that the algorithm can be converted according to the terminal when the algorithm is transplanted.

Optionally, the method may further include the step 204: the terminal prohibits selecting the target cell again within the preset duration.

After the terminal determines that the base station of the target cell is a pseudo base station, the terminal may prohibit the target cell from being selected again within a preset duration. The preset duration may be a fixed length of time, for example, 10 seconds, 1 minute, 2 minutes, etc., or may be a dynamically adjusted time length, for example, from 10 seconds to 2 minutes according to actual conditions. It can be understood that the preset duration can be determined according to actual conditions, and the application does not limit this. The terminal can identify the target cell by using the cell identifier, such as cell id, LAC+cell id, Mobile Network Code (MNC)+LAC+cell id, or Mobile Country Code (MCC)+MNC+LAC+ Cell id. The terminal may encounter the true base station cell that is impersonated, so the preset duration should be reasonable, so that the probability that the terminal encounters the impersonated real base station cell in the preset duration is very low.

The terminal prohibits the selection of the target cell again within the preset duration, and may prevent the terminal from reselecting the target cell that identifies the pseudo base station again, while preventing the true base station cell that is impersonated from being reselected. This is because some pseudo base stations will listen to the signals of the surrounding real base stations, read the system broadcast parameters of a weak base station, and transmit them after modifying some parameters (such as cell reselection parameters). Generally, the pseudo base station does not modify the cell identity of the real base station, thereby posing as the true base station. Moreover, the pseudo base station is often transferred. For example, the pseudo base station is placed on the vehicle, so that the position of the pseudo base station can be continuously changed, and the signal of the pseudo base station is covered in one area for a period of time, for example, 10 minutes, and then the pseudo base station is transferred to other units. local. In such a scenario, if the terminal is configured to prevent the target cell from being selected again after the base station of the target cell is identified as the pseudo base station, the terminal acquires the true base station impersonated by the pseudo base station after the pseudo base station of the target cell is transferred. Signal, at this time, the terminal will likely misjudge the real base station as a pseudo base station.

Optionally, the specific implementation of step 204 may also be combined with location information of the terminal. Specifically, when the terminal determines that the base station of the target cell is a pseudo base station, the terminal records the first location information where the terminal is located, and within the preset duration, the terminal acquires the second location information where the terminal is located, when the second location information and the first location information When the distance between the location information is less than the preset distance, and the terminal acquires the cell signal that the cell identifier is the cell identifier of the target cell, the terminal prohibits the cell from being selected again. This is because the terminal may move within the preset time period. If the terminal leaves the coverage area of the target cell and enters the service area of the true base station impersonated by the pseudo base station of the target cell, the terminal may stop prohibiting the reselection of the target cell. The preset distance may be, for example, 300 meters, etc., which is not limited in this application.

Optionally, after the step 202, the method of the embodiment of the present invention further includes: when the confidence is less than or equal to the fourth confidence threshold, the terminal performs a preset operation by using the target cell, where the preset operation includes network registration, Any of the location area update, cell camping, and originating service request. At this time, the base station of the target cell is a true base station, and the terminal can connect to the mobile network through the target cell. The preset operation may be an operation that needs to be performed when the terminal normally connects to the mobile network.

The fourth confidence threshold may be a preset confidence threshold, and the fourth confidence threshold is used to divide whether the base station of the target cell is a true base station. Through a reasonable setting of the fourth confidence threshold, the pseudo base station identification algorithm can satisfy the designer's performance index requirement, and the performance indicator includes, for example, a true base station identification rate. The fourth confidence threshold may be set according to the test result of the pseudo base station identification algorithm, the performance indicator of the pseudo base station identification algorithm (for example, the true base station identification rate), and the like. After the machine learning algorithm training generates the pseudo base station identification algorithm, the performance test of the algorithm can be performed. First, the fourth confidence threshold (such as 0.1) is set, and then the true base station recognition rate of the real base station cell test sample set is obtained (for example, 0.999). By adjusting the fourth confidence threshold, different algorithm performance indicators can be obtained. When the performance index of the algorithm meets the requirements of the designer, the pseudo base station identification algorithm can be transplanted to each terminal.

It can be understood that, as shown in FIG. 3, the fourth confidence threshold is smaller than the first confidence threshold. For example, the fourth confidence threshold can be set to 0.1 and the first confidence threshold is 0.9.

It can be understood that, by reasonably setting the first confidence threshold or the fourth confidence threshold, it can be determined that the base station of the cell whose confidence is greater than or equal to the first confidence threshold is a pseudo base station, and the confidence is less than or equal to the fourth confidence threshold. The base station of the cell is a true base station. However, if the confidence of the target cell is between the first confidence level and the fourth confidence level, the authenticity of the base station of the cell needs to be further identified by other means. The identification process is also a re-identification process for the suspected base station, wherein the suspected base station refers to a base station that cannot accurately identify the authenticity through the pseudo base station identification algorithm, specifically, when the confidence of the target cell is at the first confidence threshold and the fourth When the confidence threshold is between, the base station of the target cell is a suspect base station. The re-identification process for the suspected base station is described below.

1.1. Identify the suspected pseudo base station again.

In some examples, re-identification of a suspected pseudo base station is involved. After the step 202, the method of the embodiment of the present invention further includes: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal The base station of the target cell is determined to be a pseudo base station. Optionally, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, the terminal performs a preset operation by using the target cell; and when the terminal detects that the target cell meets the first preset condition, The terminal determines that the base station of the target cell is a pseudo base station. The preset operation includes any one of network registration, location area update, cell camping, and originating service request.

When the confidence level of the target cell is greater than or equal to the first confidence threshold, when the confidence of the target cell is less than the first confidence threshold, the probability that the target cell is a pseudo base station cell is reduced, and if the target cell is directly The base station is identified as a pseudo base station, and the false alarm rate of the pseudo base station is increased, which affects the user's use of the terminal. On the other hand, if the confidence is less than the second confidence threshold, the base station of the target cell is less likely to be a pseudo base station. At this time, the base station of the target cell may not participate in the re-identification process of the suspected pseudo base station.

The second confidence threshold is a preset confidence threshold. As shown in FIG. 3, the second confidence threshold is smaller than the first confidence threshold. For example, when the confidence is in the range of 0 to 1, the second confidence is The degree threshold is 0.4 and the first confidence threshold is 0.9. The setting rule of the second confidence threshold is that, in the test of the pseudo base station identification algorithm, the probability that the confidence level of the pseudo base station cell test sample falls within the interval between the second confidence threshold and the first confidence threshold satisfies the designer's Requirements (such as 0.70).

In this way, the terminal determines whether the confidence of the target cell is less than the first confidence threshold and is greater than or equal to the second confidence threshold, that is, whether the confidence of the target cell is located between the first confidence threshold and the second confidence threshold. . If the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, indicating that the base station of the target cell is a suspected pseudo base station, for example, the second confidence threshold is 0.4, and the first confidence threshold is 0.9, and the target is The confidence level of the cell is 0.7, and the base station of the target cell is a suspected pseudo base station. Therefore, the terminal further needs to detect the target cell, that is, the terminal detects whether the target cell meets the first preset condition. If the target cell meets the first preset condition, the terminal may determine that the base station of the target cell is a pseudo base station.

The first preset condition may be behavior information that may be generated by the pseudo base station cell, and the first preset condition may include at least one condition that the terminal intercepts the problem message sent by the target cell; when the terminal initiates the location area update request to the target cell, Rejected; the terminal is rejected when initiating a service request to the target cell; the terminal loses the target cell signal within a preset time; or the LAC of the target cell changes.

In an example, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, the terminal intercepts the problem message sent by the target cell through the application layer (the application layer may be located in the application processor). Then, the base station of the target cell is determined to be a pseudo base station, and is detached from the target cell; or, when the terminal is kicked out by the target cell within a certain time, the terminal determines that the base station of the target cell is a pseudo base station.

In another example, in the redirection scenario or the handover scenario, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, if the terminal rejects the cell when the service request is initiated, the terminal The base station of the target cell is determined to be a pseudo base station and detached from the target cell.

In another example, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, if the terminal does not detect the signal of the target cell within the preset time, the target cell signal is lost. Then, the terminal determines that the base station of the target cell is a pseudo base station. The preset time may be determined according to actual conditions, for example, may be 5 seconds, 7 seconds, etc., which is not limited in this application.

In another example, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold of 0.4, the terminal detects that the LAC of the target cell has changed, and the terminal may also determine the base station of the target cell. It is a pseudo base station.

In some other examples, the first preset condition may also be any two or any three of the above conditions or a combination of any four or all five.

These conditions are all possible behaviors of the pseudo base station cell. If the specific condition of the first preset condition that the target cell satisfies, the probability that the base station of the target cell is a pseudo base station is larger. In a specific application, the first preset condition may be set according to actual conditions.

Optionally, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal determines that the base station of the target cell is a pseudo base station. The method includes: the terminal saves feature data of the target cell, and the feature data of the target cell is identified as pseudo base station data.

The mapping of the feature data of the target cell to the pseudo base station data may be implemented by configuring the pseudo base station identity information for the feature data of the target cell, for example, establishing a correspondence between the feature data of the target cell and the pseudo base station identity information, or The feature data of the target cell is stored in a preset storage area or the like. For example, a database is set on the memory of the terminal for storing feature data of the target cell, and the feature data stored in the database belongs to the pseudo base station data.

The confidence of the target cell is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, indicating that the pseudo base station identification algorithm is not ideal for identifying the feature data of the target cell. To this end, the terminal save is identified as a pseudo. Characteristic data of the target cell of the base station data. In this way, the pseudo base station identification algorithm can be further trained according to the saved feature data of the target cell, thereby improving the recognition rate of the pseudo base station by the pseudo base station identification algorithm. For example, when the terminal is connected to a WiFi (Wireless-Fidelity) network, the terminal transmits the feature data of the target cell identified as the pseudo base station data to the cloud server through the WiFi network, and the cloud server uses the feature data of the target cell as a pseudo. The sample data of the base station cell further trains a pseudo base station identification algorithm pre-stored on the cloud server to update the pseudo base station identification algorithm. The pseudo base station algorithm pre-stored on the cloud server may be the pseudo base station identification algorithm described in step 202. Thus, the updated pseudo base station identification algorithm has a higher recognition rate for the pseudo base station. Optionally, the cloud server may further send the updated pseudo base station identification algorithm to the terminal, so that the terminal may use the updated pseudo base station identification algorithm to identify the pseudo base station. Training the pseudo base station identification algorithm according to the feature data of the target cell can be performed on the cloud server.

In the embodiment of the present invention, the feature data of the terminal to save the target cell may be performed when any of the following conditions are met: the pseudo base station data saving function is enabled; or the target cell satisfies the pseudo base station data storage rule. In one example, when the pseudo base station data saving function is turned on, the terminal saves the feature data of the target cell. In other words, the terminal determines whether the pseudo base station data saving function is enabled. If enabled, the terminal may save the feature data of the target cell; otherwise, the terminal may not save the feature data of the target cell.

The terminal can control the opening and closing of the pseudo base station data saving function. The opening and closing of the pseudo base station data saving function may be set for the user on the terminal, or may be set by the cloud server to send instructions to the terminal, or may be automatically controlled by the terminal according to a preset rule. For example, the user may set the terminal to save or not save the feature data of the target cell; or the cloud server may or may not save the feature data of the target cell by using the remote command; or, the storage space of the terminal is less than a preset storage threshold. The rule is that when the storage space of the terminal is less than a preset storage threshold, the terminal automatically turns off the pseudo base station data saving function. The preset storage threshold may be determined according to actual conditions, which is not limited in this application. Thereby, the terminal can conveniently control the storage of the feature data, and the flexibility of saving the feature data is increased.

In another example, when the target cell satisfies the pseudo base station data saving rule, the terminal saves the feature data of the target cell. In this way, the function of screening the feature data can be played, so that the feature data of the saved target cell is more advantageous for further training of the pseudo base station identification algorithm.

The pseudo base station data storage rule may be a rule for screening feature data of the pseudo base station cell. The pseudo base station data storage rule may be a rule set by the user on the terminal, or may be a rule sent by the cloud server to the terminal. The pseudo base station data retention rule may be, for example, an acquisition time of feature data of the target cell, a location area where the target cell is located, and certain feature data of the target cell, and the like.

The specific implementation of the example may be: the terminal acquires target data applicable to the current pseudo base station data retention rule from the feature data of the target cell and other data, and then the terminal determines whether the target data conforms to the pseudo base station data retention rule. If it matches, the feature data of the target cell is saved.

As an example, the cloud server sends an instruction to the terminal to indicate to the terminal that the pseudo base station data storage rule is to save the feature data of the pseudo base station cell located in the Shenzhen area of China. The location of the Shenzhen area may be described by a latitude and longitude or a network identifier such as MNC+LAC. When the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal determines the location area where the target cell is located, when the target cell is located When the location information is in the Shenzhen area of China, the target cell conforms to the pseudo base station data storage rule, and the terminal saves the feature data of the target cell, and the feature data is identified as pseudo base station data.

As another example, the pseudo base station data retention rule is that the collection time of the feature data of the target cell is a weekend, when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell is satisfied. When the first preset condition is met, the terminal determines the collection time of the feature data of the target cell from the related information of the target cell. When the collection time is a weekend, the target cell meets the data storage rule of the pseudo base station, and the terminal saves the feature data of the target cell. The feature data is identified as pseudo base station data.

As another example, the pseudo base station data saving rule is to save cell selection and cell reselection information, and networking information. When the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal determines the type of the feature data of the target cell, when the characteristics of the target cell When the data is cell selection, cell reselection information, and networking information, the target cell conforms to the pseudo base station data storage rule, and the terminal stores feature data of the target cell, and the feature data is identified as pseudo base station data.

In other examples, when the pseudo base station data saving function is enabled and the target cell satisfies the pseudo base station data saving rule, the terminal saves the feature data of the target cell. That is, when the conditions described in the two examples above are satisfied, the terminal saves the feature data of the target cell.

The method of recognizing the suspected pseudo base station again is described above, and the following describes the method of recognizing the suspected true base station again.

1.2. Identify the suspected true base station again.

In some examples, re-identification of suspected true base stations is involved. After the step 202, the method of the embodiment of the present invention further includes: when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, the terminal It can be determined that the base station of the target cell is a true base station. Optionally, when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, the terminal performs a preset operation by using the target cell; and when the terminal detects that the target cell meets the second preset condition, The terminal determines that the base station of the target cell is a true base station. The preset operation includes any one of network registration, location area update, cell camping, and originating service request.

When the confidence level of the target cell is less than or equal to the fourth confidence threshold, when the confidence of the target cell is greater than the fourth confidence threshold, the probability that the target cell is a true base station cell is reduced, and if the target cell is directly Base station When it is identified as a true base station, the authenticity of the base station is easily misidentified, which affects the user's use of the terminal. On the other hand, if the confidence level is greater than the third confidence threshold, the base station of the target cell is less likely to be a true base station. At this time, the base station of the target cell may not participate in the re-identification process of the suspected true base station.

The third confidence threshold is a preset confidence threshold. As shown in FIG. 3, the third confidence threshold is greater than the fourth confidence threshold. For example, when the confidence is in the range of 0 to 1, the third confidence. The degree threshold is 0.6 and the fourth confidence threshold is 0.4. The setting rule of the third confidence threshold is: in the test of the pseudo base station identification algorithm, the probability that the confidence of the true base station cell test sample falls within the fourth confidence threshold and the third confidence threshold interval satisfies the designer's requirements (for example 0.20).

In this way, the terminal determines whether the confidence of the target cell is greater than the fourth confidence threshold and is less than or equal to the third confidence threshold, that is, whether the confidence of the target cell is between the fourth confidence threshold and the third confidence threshold. If the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, the base station indicating the target cell is a suspected true base station. Therefore, the terminal needs to further detect the target cell, that is, the terminal detects whether the target cell meets the second preset condition. If the target cell meets the second preset condition, the terminal may determine that the base station of the target cell is a true base station.

The second preset condition may be behavior information that may be generated by the real base station cell, and the second preset condition may include at least one of the following conditions: the terminal establishes a call or data service in the target cell; the terminal completes the authentication through the target cell and enters the encryption security mode. Or, the terminal completes the handover in the target cell.

In an example, when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, if the terminal establishes a call or data service in the target cell, the terminal determines that the base station of the target cell is a true base station.

In another example, when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, if the terminal completes the authentication through the target cell and enters the encryption security mode, the terminal determines that the base station of the target cell is true. Base station.

In another example, when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, if the terminal completes the handover in the target cell, the terminal determines that the base station of the target cell is a true base station. In other examples, the second preset condition may also be a combination of any two or all three of the above conditions.

These conditions are all possible behaviors of a true base station cell. If the specific condition of the second preset condition that the target cell satisfies, the probability that the base station of the target cell is a true base station is larger. In a specific application, the second preset condition may be set according to actual conditions.

Optionally, when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, the terminal determines that the base station of the target cell is a true base station. The method includes: the terminal saves feature data of the target cell, and the feature data of the target cell is identified as true base station data.

The feature data of the target cell is identified as true base station data, which may be implemented by configuring true base station identification information for the feature data of the target cell, or saving the feature data of the target cell in a preset storage area.

The confidence of the target cell is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, indicating that the pseudo base station identification algorithm does not have an ideal recognition effect on the feature data of the target cell. To this end, the terminal save is identified as true. Characteristic data of the target cell of the base station data. In this way, the pseudo base station identification algorithm can be further trained according to the saved feature data of the target cell, and the recognition rate of the pseudo base station identification algorithm to the true base station is improved. Correspondingly, the recognition rate of the real base station by the pseudo base station identification algorithm is improved, and the false alarm rate of the pseudo base station is also reduced. Training pseudo based on the feature data of the target cell The base station identification algorithm can be performed on the cloud server.

In the embodiment of the present invention, the feature data of the terminal to save the target cell may be performed when any of the following conditions are met: the true base station data saving function is enabled; or the target cell satisfies the true base station data storage rule.

In one example, when the true base station data saving function is turned on, the terminal saves the feature data of the target cell. For a specific implementation of the example, reference may be made to the above description for identifying the suspected pseudo base station again, and details are not described herein.

In another example, when the target cell satisfies the true base station data retention rule, the terminal saves the feature data of the target cell. In this way, the function of screening the feature data can be played, so that the feature data of the saved target cell is more advantageous for further training of the pseudo base station identification algorithm.

The true base station data storage rule may be a rule for screening feature data of a true base station cell. The real base station data storage rule may be a rule set by the user on the terminal, or may be a rule sent by the cloud server to the terminal. The true base station data retention rule may be, for example, an acquisition time of feature data of the target cell, a location area where the target cell is located, and certain feature data of the target cell, and the like. For a specific implementation of the data storage rule of the real base station, reference may be made to the description of identifying the suspected pseudo base station again, and details are not described herein. It can be understood that the true base station data saving rule can be the same as or different from the pseudo base station data saving rule.

In other examples, when the true base station data saving function is enabled and the target cell satisfies the true base station data saving rule, the terminal saves the feature data of the target cell. That is, when the conditions described in the two examples above are satisfied, the terminal saves the feature data of the target cell.

It can be understood that, in the process of judging the above confidence degree, “less than or equal to” is not substantially different from “less than”, and may be equivalently replaced. “greater than or equal to” is not substantially different from “greater than”, and may be equivalently replaced. It can be understood that, regarding the magnitude relationship between the first confidence threshold, the second confidence threshold, the third confidence threshold, and the fourth confidence threshold, refer to FIG. 3, where the second confidence threshold may be less than the third confidence threshold. The second confidence threshold may be greater than or equal to a fourth confidence threshold, and the third confidence threshold may be less than or equal to the first confidence threshold. In some examples of the present application, the confidence of the target cell may be located in the interval between the second confidence threshold and the third confidence threshold. At this time, the terminal may detect whether the target cell meets the first preset condition and also detects the target. Whether the cell satisfies the second preset condition. When the terminal detects that the target cell meets the first preset condition, the terminal may determine that the base station of the target cell is a pseudo base station; when the terminal detects that the target cell meets the second preset condition, the terminal determines that the base station of the target cell is a true base station. In summary, in order to access the mobile network, change the camping cell or switch the cell, the terminal selects the target cell through a standard defined process, and after the terminal acquires the feature data of the target cell, the terminal performs authenticity identification for the target cell. The terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and obtains a confidence level. When the confidence level is greater than or equal to the first confidence threshold, the base station indicating that the target cell is a pseudo base station has a great possibility. Therefore, the terminal can reasonably determine that the base station of the target cell is a pseudo base station, and perform corresponding operations to Avoid the danger of pseudo base stations. The pseudo base station identification algorithm is generated by the training of the machine learning algorithm, so that after the pseudo base station identification algorithm is run according to the feature data of the target cell, the confidence degree is obtained, and the base station of the target cell is authenticated by the confidence degree. The pseudo base station identification algorithm has the performance advantage of the machine learning algorithm, can improve the recognition performance of the pseudo base station, and the pseudo base station identification algorithm can continuously train and quickly follow up the pseudo base station technology evolution, thereby improving the recognition success rate of the pseudo base station.

FIG. 4 is a flowchart of a method for identifying a pseudo base station according to an embodiment of the present invention. The square of the embodiment shown in Figure 4 The method can be implemented based on the method of the embodiment shown in FIG. 2. The scenario in which the method of the embodiment shown in FIG. 4 is applicable can also refer to FIG. 5. FIG. 5 is a schematic diagram of a scenario structure involved in a pseudo base station identification method. In this scenario, a terminal 501, a terminal 502, a cloud server 503, and a base station 504 are included. For specific contents of the terminal 501, the terminal 502, and the base station 504, reference may be made to the corresponding description of the base station 101 and the terminal 102 in the embodiment shown in FIG. 1a.

In the scenario shown in FIG. 5, the terminal 501 and the terminal 502 can also communicate with the cloud server 503. Specifically, the terminal 501 can collect sample data of the cell, and send the sample data to the cloud server 503. After receiving the cell sample data sent by the terminal 501, the cloud server 503 can use the sample data to train the machine learning algorithm to obtain a pseudo base station identification algorithm. Then, the cloud server 503 transmits the pseudo base station identification algorithm to the terminal 502 to cause the terminal 502 to execute the pseudo base station identification method of the embodiment shown in FIG. 2. The sample data of the cell may include sample data of the pseudo base station cell and sample data of the true base station cell. The sample data of the pseudo base station cell may be sample data of a target cell whose confidence is greater than or equal to the first confidence threshold, or may be sample data of the pseudo base station cell identified by the suspected pseudo base station again. Similarly, the sample data of the true base station cell may be sample data of a target cell whose confidence is less than a fourth confidence threshold, or may be sample data of a true base station cell that is identified by the suspected true base station.

It can be understood that FIG. 5 is merely an exemplary illustration. In other examples, the terminal 501 may have the function of the terminal 502, or the terminal 502 may also have the function of the terminal 501.

In the following, the method for identifying the pseudo base station in the embodiment shown in FIG. 4 is described in detail. Referring to the content of the foregoing embodiments, the method in the embodiment of the present invention includes:

Step 401: The terminal acquires sample data of a real base station cell and sample data of a pseudo base station cell.

Before the terminal obtains the pseudo base station identification algorithm, the cloud server needs to collect sample data through the terminal, and uses the sample data to train the machine learning algorithm to generate a pseudo base station identification algorithm.

The base station of the real base station cell is a true base station, and the base station of the pseudo base station cell is a pseudo base station.

The specific implementation manner of the terminal acquiring the sample data of the real base station cell may be: the terminal successfully establishes a call or data service in a certain cell, or performs authentication and enters an encryption security mode, or the cell handover succeeds, where the situation indicates that the cell is a true base station cell. Therefore, the terminal may extract sample data of the cell from the signal broadcast by the cell base station, and mark the sample data of the cell as the sample data of the true base station cell.

The specific implementation manner of the terminal acquiring the sample data of the pseudo base station cell may be: the terminal identifies the pseudo base station cell by using the feature data matching algorithm; or, after the terminal camps on the certain cell, if the application layer of the terminal identifies the harassment message sent by the cell And the modem chip of the terminal further detects that the terminal is kicked out of the cell within a certain time (for example, 3 minutes), and the foregoing situation indicates that the cell is a pseudo base station cell, and therefore, the terminal may extract the cell from the signal broadcast by the cell base station. The sample data, and the sample data of the cell is marked as sample data of the pseudo base station cell.

In the embodiment of the present invention, the sample data of the real base station cell and the sample data of the pseudo base station cell may be at least one of the following information: cell selection and cell reselection information, networking information, service function information, area information, and time information. Wait. For a detailed description of the information, refer to step 202, and details are not described herein again.

It can be understood that the sample data of the terminal acquiring the real base station cell and the sample data of the pseudo base station cell may be controlled by the collection policy, and the collection policy may be set by the user, or may be obtained by the terminal from the cloud server. The acquisition policy may be, for example, whether to save the sample data of the real base station cell or the sample data of the pseudo base station cell; or the terminal collects the sample data of the cell in the preset area, and the terminal collects the sample data of the cell in a preset time period. In some examples, The acquisition policy may be a pseudo base station data retention rule and/or a true base station data retention rule as described in the embodiment shown in FIG. 2.

Step 402: The terminal sends the sample data of the true base station cell and the sample data of the pseudo base station cell to the cloud server.

After the terminal acquires the sample data of the real base station cell and the sample data of the pseudo base station cell, the terminal sends the sample data of the true base station cell and the sample data of the pseudo base station cell to the cloud server, so that the cloud server uses the data.

For example, the terminal may send the sample data of the true base station cell and the sample data of the pseudo base station cell to the cloud server by using, for example, a cellular mobile network or a WiFi network, or use the memory card to sample data of the true base station cell on the terminal. The sample data of the pseudo base station cell is transferred to the cloud server.

Step 403: The cloud server trains the machine learning algorithm by using the sample data of the real base station cell and the sample data of the pseudo base station cell to generate a pseudo base station identification algorithm.

After the cloud server obtains the sample data of the authentic base station cell, the cloud server uses the sample data of the true base station cell and the sample data of the pseudo base station cell to perform training of the machine learning algorithm to generate a pseudo base station identification algorithm. The training of the machine learning algorithm can be based on big data training.

The pseudo base station identification algorithm can be used to identify the authenticity of the target cell.

For machine learning algorithm training, clustering algorithm or classification algorithm, such as K-means, k-nearest neighbor, decision tree, logistic regression, SVM, Bayesian algorithm, etc., can be used to train and generate a calculation model, which is Pseudo base station identification algorithm.

For example, the cloud server uses the sample data of the real base station cell and the sample data of the pseudo base station cell to perform training of the SVM algorithm, and obtains an SVM model instance, where the SVM model instance is a pseudo base station identification algorithm, which can be used according to the feature data of the target cell. The sample data of the target cell is identified, and it is determined whether the base station of the target cell is a true base station or a pseudo base station.

With the help of the machine learning domain method, the selected machine learning algorithm can establish the authentic base station identification capability (or the authentic base station knowledge) through a large number of sample training, and the recognition capability can be included in the trained generated algorithm.

Optionally, the method of the embodiment of the present invention further includes: the cloud server tests the pseudo base station identification algorithm, and when the pseudo base station identification algorithm meets the performance requirement of the designer, the cloud server sends the pseudo base station identification algorithm to the terminal.

In an example, after the cloud server obtains the sample data of the true base station cell and the sample data of the pseudo base station cell sent by the terminal, the cloud server reserves a part of the sample data for testing the algorithm to obtain the performance of the pseudo base station identification algorithm. . The reserved sample data may be the true base station test sample set or the pseudo base station test sample set of the embodiment shown in FIG. 2. Specifically, after the cloud server is trained to generate the pseudo base station identification algorithm, the sample data of the reserved true base station cell and the sample data of the pseudo base station cell are input to the pseudo base station identification algorithm, and the pseudo base station identification algorithm is used for the identification test. In this way, the probability that the confidence of the pseudo base station identification algorithm for the sample data output of the pseudo base station cell is above the first test confidence threshold (eg, 0.9) (which may be referred to as a pseudo base station identification rate) may be obtained, and the pseudo base station identification algorithm is true. The probability that the confidence of the sample data output of the base station cell is above the first test confidence threshold (which may be referred to as a pseudo base station false alarm rate) may also obtain the confidence that the pseudo base station identification algorithm outputs the sample data of the true base station cell. The probability of the fourth test confidence threshold (eg, 0.4) or less (which may be referred to as a true base station identification rate).

In another example, when the pseudo base station identification algorithm reaches the designer's performance metric (eg, the pseudo base station identification rate) 0.8, the pseudo base station false alarm rate is 0.002, and the true base station identification rate is 0.99), the pseudo base station identification algorithm is an algorithm that recognizes that the effect meets the requirements.

Step 404: The cloud server sends a pseudo base station identification algorithm to the terminal.

After obtaining the pseudo base station identification algorithm, the cloud server may send the pseudo base station identification algorithm to the terminal, that is, the pseudo base station identification algorithm is transplanted to the terminal. Thus, the terminal can identify the pseudo base station using the pseudo base station identification algorithm. In some examples, the cloud server may transcode according to the terminal before transmitting the pseudo base station identification algorithm.

It can be understood that the terminal in step 404 and the terminal in step 401 can be the same terminal or different terminals, which is not limited in this application.

The following describes how the terminal uses the pseudo base station identification algorithm.

Step 405: The terminal selects a target cell. Step 406: The terminal runs the pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level.

Step 407: When the confidence is greater than or equal to the first confidence threshold, the terminal determines that the base station of the target cell is a pseudo base station.

Step 408: The terminal prohibits selecting the target cell again within the preset duration.

The specific implementation of step 405 to step 408 can be referred to the detailed description of step 201 to step 204, respectively.

Optionally, after the step 406, the method of the embodiment of the present invention further includes: when the confidence is less than or equal to the fourth confidence threshold, the terminal performs a preset operation by using the target cell, where the preset operation includes network registration, Any of the location area update, cell camping, and originating service request. At this time, the base station of the target cell is a true base station, and the terminal can connect to the mobile network through the target cell. The preset operation may be an operation that needs to be performed when the terminal normally connects to the mobile network. For the value of the fourth confidence threshold, refer to the foregoing description, and details are not described herein again.

Optionally, after the step 406, the method of the embodiment of the present invention further includes: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset In the condition, the terminal determines that the base station of the target cell is a pseudo base station. It can be understood that when the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, the base station of the target cell is a suspected pseudo base station, and thus the suspected pseudo base station can be re-identified in combination with the first preset condition. For the method of identifying the suspected pseudo base station again, refer to the description of “1.1 Identifying the suspected pseudo base station again”, and details are not described herein again.

Optionally, after the step 406, the method of the embodiment of the present invention further includes: when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset When the condition is met, the terminal determines that the base station of the target cell is a true base station. It can be understood that when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, the base station of the target cell is a suspected true base station, and thus the suspected true base station can be re-identified in combination with the second preset condition. For the method of identifying the suspected true base station again, refer to the description of “1.2 Identifying the suspected true base station again”, and details are not described herein again.

By saving the feature data of the target cell identified as the pseudo base station data, and saving the feature data of the target cell identified as the true base station data, the terminal may be in a preset situation, for example, when connecting to the WiFi network, Send the saved feature data to the cloud server. The cloud server uses the feature data of the target cell identified as the pseudo base station data as the sample data of the pseudo base station cell, or the feature data of the target cell identified as the true base station data as the sample data of the true base station cell, and the pre-stored pseudo base station The recognition algorithm is further trained to update the pseudo base station Do not algorithm. Thus, the updated pseudo base station identification algorithm has a higher recognition rate for the authentic base station. Optionally, the cloud server may further send the updated pseudo base station identification algorithm to the terminal for use by the terminal.

It should be noted that the pseudo base station identification algorithm based on big data and machine learning essentially needs to continuously train and iterate the pseudo base station identification algorithm by using the sample data of the authentic cell, thereby adapting to the changes of the new pseudo base station and the real base station. The authenticity base station sample collection and algorithm training can be continued.

FIG. 6 is a schematic structural diagram of a terminal according to an embodiment of the present disclosure, where the terminal is used to perform the method performed by the terminal in the foregoing embodiment shown in FIG. 2 and FIG. 4. Referring to FIG. 6, the terminal of the embodiment of the present invention includes a selecting unit 601, an operating unit 602, and a determining unit 603.

The selecting unit 601 is configured to select a target cell, and the running unit 602 is configured to run a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level, where the confidence level is used to indicate that the base station of the target cell is a trusted base station. The pseudo base station identification algorithm is generated by the machine learning algorithm training. The determining unit 603 is configured to determine that the base station of the target cell is a pseudo base station when the confidence level is greater than or equal to the first confidence threshold.

Optionally, the determining unit 603 is further configured to: when the confidence level is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, determining the target cell The base station is a pseudo base station.

Optionally, the determining unit 603 includes a saving module 604.

The saving module 604 is configured to save the feature data of the target cell when the confidence level is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, The feature data of the target cell is identified as pseudo base station data.

Optionally, the saving module 604 is further configured to: when the pseudo base station data saving function is enabled, save the feature data of the target cell; or, when the target cell satisfies the pseudo base station data storage rule, save the feature data of the target cell; or When the pseudo base station data saving function is enabled and the target cell satisfies the pseudo base station data storage rule, the feature data of the target cell is saved.

Optionally, the first preset condition includes at least one condition that the terminal intercepts the problem message sent by the target cell; when the terminal initiates the location area update request to the target cell, the terminal is rejected; when the terminal initiates the service request to the target cell, the terminal is rejected; The target cell signal is lost within a preset time; or, the location area code LAC of the target cell changes.

Optionally, the terminal further includes an executing unit 605;

The executing unit 605 is configured to perform a preset operation by using the target cell when the confidence is less than or equal to the fourth confidence threshold, where the preset operation includes any one of network registration, location area update, cell camping, and originating service request. .

Optionally, the determining unit 603 is further configured to: when the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, determining the target cell Base station is the base station.

Optionally, the saving module 604 is further configured to save the target cell when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition. Characteristic data, the feature data of the target cell is identified as true base station data.

Optionally, the saving module 604 is further configured to: save the feature data of the target cell when the true base station data saving function is enabled; or save the feature data of the target cell when the target cell satisfies the true base station data saving rule; or, the authentic base station When the data saving function is enabled and the target cell satisfies the true base station data storage rule, the feature data of the target cell is saved.

Optionally, the second preset condition includes at least one condition that the terminal establishes a call or a data service in the target cell; the terminal completes the authentication through the target cell and enters an encryption security mode; or the terminal completes the handover in the target cell.

Optionally, the target cell is a Global System for Mobile Communications (GSM) cell.

Optionally, the running unit 602 is further configured to: when the terminal detects that the LAC of the target cell is different from the currently saved LAC, run the pseudo base station identification algorithm according to the feature data of the target cell, to obtain a confidence level.

Optionally, after the terminal determines that the base station of the target cell is a pseudo base station, the selecting unit 601 is further configured to prohibit the target cell from being selected again within the preset duration.

Optionally, the feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information.

Optionally, the selecting unit 601 is configured to perform step 201, step 204, step 405, and step 408 above.

Optionally, the running unit 602 is configured to perform step 202 and step 406 above.

Optionally, the determining unit 603 is configured to perform step 203 and step 407 in the foregoing.

FIG. 7 is a schematic structural diagram of a hardware structure of a terminal according to an embodiment of the present invention. The terminal shown in FIG. 7 can be used to perform the method shown in FIG. 2 and FIG. 4. The terminal shown in FIG. 6 can be integrated in FIG. On the terminal.

For the convenience of description, only the parts related to the embodiments of the present invention are shown. If the specific technical details are not disclosed, please refer to the method part of the embodiment of the present invention. The terminals involved in the embodiments of the present invention may include various handheld devices having wireless communication functions, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to the wireless modem. The terminal may also be referred to as a mobile station (MS), a user equipment, a terminal device, and may also include a subscriber unit and a cellular phone. , smart phone, wireless data card, personal digital assistant (PDA) computer, tablet computer, wireless modem (modem), handheld, laptop computer, cordless Cordless phone or wireless local loop (WLL) station, machine type communication (machine type communication, MTC) terminal, etc. In the embodiment of the present invention, the terminal is used as a mobile phone as an example for description.

As shown in FIG. 7, the mobile phone includes: a radio frequency (RF) circuit 710, a memory 720, an input unit 730, a display 740, a sensor 750, an audio circuit 760, a wireless fidelity (WiFi) module 770, and a processor. 780, and power supply 790 and other components. It will be understood by those skilled in the art that the structure of the handset shown in FIG. 7 does not constitute a limitation to the handset, and may include more or less components than those illustrated, or some components may be combined, or different components may be arranged.

The following describes the components of the mobile phone in detail with reference to FIG. 7:

The RF circuit 710 can be used for transmitting and receiving information or during a call, and receiving and transmitting the signal. Specifically, after receiving the downlink information of the base station, the processor 780 processes the data. In addition, the uplink data is designed to be sent to the base station. Generally, RF circuit 710 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, RF circuitry 710 can also communicate with the network and other devices via wireless communication. The above wireless communication may use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (Code Division). Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), E-mail, Short Messaging Service (SMS), and the like.

The memory 720 can be used to store software programs and modules, and the processor 780 executes various functional applications and data processing of the mobile phone by running software programs and modules stored in the memory 720. The memory 720 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of the mobile phone (such as audio data, video data, phone book, etc.). In addition, the memory 720 may include volatile memory such as random access memory (RAM), nonvolatile random access memory (NVRAM), phase change random access memory (Phase Change). RAM, PRAM), magnetoresistive random access memory (MRAM), etc., may also include non-volatile memory, such as at least one disk storage device, read-only memory (ROM), electronically Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory devices such as NOR flash memory or NAND flash memory, semiconductor devices such as solid state hard disks (Solid) State Disk, SSD), etc.

The input unit 730 can be configured to receive input numeric or character information and to generate key signal inputs related to user settings and function controls of the handset. Specifically, the input unit 730 may include a touch panel 731 and other input devices 732. The touch panel 731, also referred to as a touch screen, can collect touch operations on or near the user (such as the user using a finger, a stylus, or the like on the touch panel 731 or near the touch panel 731. Operation), and drive the corresponding connecting device according to a preset program. Alternatively, the touch panel 731 may include two parts of a touch detection device and a touch controller. Wherein, the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information. The processor 780 is provided and can receive commands from the processor 780 and execute them. In addition, the touch panel 731 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to touch Panel 731, input unit 730 may also include other input devices 732. In particular, other input devices 732 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, joysticks, and the like.

Display 740 can be used to display information entered by the user or information provided to the user as well as various menus of the handset. The display panel 740 can include a display panel 741. Alternatively, the display panel 741 can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like. Further, the touch panel 731 can cover the display panel 741. When the touch panel 731 detects a touch operation on or near the touch panel 731, it transmits to the processor 780 to determine the type of the touch event, and then the processor 780 according to the touch event. The type provides a corresponding visual output on display panel 741. Although the touch panel 731 and the display panel 741 are used as two independent components to implement the input and input functions of the mobile phone in FIG. 7, in some embodiments, the touch panel 731 can be integrated with the display panel 741. Realize the input and output functions of the phone.

The handset may also include at least one type of sensor 750, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 741 according to the brightness of the ambient light, and the proximity sensor may close the display panel 741 and/or when the mobile phone moves to the ear. Or backlight. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity. It can be used to identify the gesture of the mobile phone (such as horizontal and vertical screen switching, related Game, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for the mobile phone can also be configured with gyroscopes, barometers, hygrometers, thermometers, infrared sensors and other sensors, no longer Narration.

An audio circuit 760, a speaker 761, and a microphone 762 can provide an audio interface between the user and the handset. The audio circuit 760 can transmit the converted electrical data of the received audio data to the speaker 761 for conversion to the sound signal output by the speaker 761; on the other hand, the microphone 762 converts the collected sound signal into an electrical signal by the audio circuit 760. After receiving, it is converted into audio data, and then processed by the audio data output processor 780, sent to, for example, another mobile phone via the RF circuit 710, or outputted to the memory 720 for further processing.

WiFi is a short-range wireless transmission technology, and the mobile phone can help users to send and receive emails, browse web pages, and access streaming media through the WiFi module 770, which provides users with wireless broadband Internet access. Although FIG. 7 shows the WiFi module 770, it can be understood that it does not belong to the essential configuration of the mobile phone, and may be omitted as needed within the scope of not changing the essence of the invention.

The processor 780 is the control center of the handset, which connects various portions of the entire handset using various interfaces and lines, by executing or executing software programs and/or modules stored in the memory 720, and invoking data stored in the memory 720, The phone's various functions and processing data, so that the overall monitoring of the phone. The processor 780 can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), and a field programmable gate array ( Field Programmable Gate Array (FPGA) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. The processor 780 can implement or perform various exemplary logical blocks, modules and circuits described in connection with the present disclosure. Processor 780 can also be a combination of computing functions, for example, including one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like. Optional processor 780 can include one or more processing units. Optionally, the processor 780 can integrate an application processor and a modem, wherein the application processor primarily processes an operating system, a user interface, an application, etc., and the modem processor primarily processes wireless communications. It will be appreciated that the above described modem processor may also be present independently, not integrated into the processor 780, or integrated with the audio circuit 760 or the like.

The handset also includes a power source 790 (such as a battery) that powers the various components. Optionally, the power supply can be logically coupled to the processor 780 through a power management system to manage functions such as charging, discharging, and power management through the power management system.

It should be noted that, although not shown, the mobile phone may further include a camera, a Bluetooth module, and the like, and details are not described herein.

In the embodiment of the present invention, the processor 780 may be configured to: select a target cell; run a pseudo base station identification algorithm according to the feature data of the target cell, and obtain a confidence level, where the confidence level is used to indicate that the base station of the target cell is a trusted base station. To the extent, the pseudo base station identification algorithm is generated by the machine learning algorithm training; when the confidence level is greater than or equal to the first confidence threshold, the base station of the target cell is determined to be a pseudo base station.

Optionally, the processor 780 may be further configured to determine the target cell when the confidence level is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the target cell is detected to meet the first preset condition. The base station is a pseudo base station.

Optionally, the processor 780 may be configured to: when the confidence level is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the target cell is detected to meet the first preset condition, save the target cell Characteristic data, the feature data of the target cell is identified as pseudo base station data. Alternatively, the processor 780 can control the memory 720 to save feature data of the target cell.

Optionally, the processor 780 is further configured to: save the feature data of the target cell when the pseudo base station data saving function is enabled; or save the feature data of the target cell when the target cell satisfies the pseudo base station data saving rule; or When the pseudo base station data saving function is enabled, and the target cell satisfies the pseudo base station data storage rule, the feature data of the target cell is saved. Alternatively, the processor 780 can control the memory 720 to save feature data of the target cell.

Optionally, the processor 780 is further configured to perform a preset operation by using the target cell when the confidence is less than or equal to the fourth confidence threshold, where the preset operation includes network registration, location area update, cell camping, and initiation. Any of the business requests.

Optionally, the processor 780 is further configured to: when the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the target cell is detected to meet the second preset condition, determining the target cell The base station is a true base station.

Optionally, the processor 780 is further configured to: when the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the target cell is detected to meet the second preset condition, save the target cell. Characteristic data, the feature data of the target cell is identified as true base station data. Alternatively, the processor 780 can control the memory 720 to save feature data of the target cell.

Optionally, the processor 780 is further configured to: save the feature data of the target cell when the true base station data save function is enabled; or save the feature data of the target cell when the target cell meets the true base station data save rule; or When the true base station data saving function is enabled and the target cell satisfies the true base station data storage rule, the feature data of the target cell is saved. Alternatively, the processor 780 can control the memory 720 to save feature data of the target cell.

Optionally, the processor 780 is further configured to: when detecting that the LAC of the target cell is different from the currently saved LAC, run the pseudo base station identification algorithm according to the feature data of the target cell, to obtain a confidence level.

Optionally, the processor 780 is further configured to: after determining that the base station of the target cell is a pseudo base station, prohibiting to select the target cell again within a preset time period.

Optionally, the processor 780 may also be configured to perform steps 201 to 204, and steps 401 to 408 above.

In the embodiment of the present invention, by running a pseudo-base station identification algorithm based on machine learning, the terminal may obtain a confidence according to the feature data of the target cell, and determine whether the base station of the target cell is a pseudo base station according to the confidence level. The pseudo base station identification algorithm generated by a large number of feature data training improves the recognition rate of the terminal to the pseudo base station. Moreover, the terminal may send the feature data of the suspected base station to the cloud server, and the cloud server updates the pseudo base station identification algorithm, thereby continuously following the technical evolution of the pseudo base station, and continuously improving the recognition rate of the pseudo base station.

The embodiment of the invention further provides a chip device, the chip comprising a processing unit for performing the method shown in FIG. 2 and FIG. 4 above.

The embodiment of the invention further provides a chip device, which comprises a processor and a memory. The memory includes instructions that are executed by the processor for performing the methods illustrated in Figures 2 and 4 above.

In the embodiment of the present invention, the chip device may be a chip in the terminal, the chip includes: a processing unit and a communication unit, and the processing unit may be, for example, a processor, and the processor may be various types as described above. Processor 780. The communication unit may be, for example, an input/output interface, a pin or a circuit, etc., and the communication unit includes a system bus. Optionally, the chip further includes a storage unit, where the storage unit may be a memory inside the chip, such as a register, a cache, a random access memory (RAM), an EEPROM or a FLASH, etc.; The unit may also be a memory located external to the chip, which may be various types of memory 720 as previously described. The processor is coupled to a memory that can execute instructions stored in the memory to cause the chip device to perform the methods illustrated in Figures 2 and 4 above.

In the above-described embodiments of the present invention, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present invention are generated in whole or in part. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be from a website site, computer, server or data center Transfer to another website site, computer, server, or data center by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL), or wireless (eg, infrared, wireless, microwave, etc.). The computer readable storage medium can be any available media that can be stored by a computer or a data storage device such as a server, data center, or the like that includes one or more available media. The usable medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

The specific embodiments described above further detail the object, technical solution and beneficial effects of the present invention. Description. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.

Claims

A pseudo base station identification method, comprising:

The terminal selects a target cell;

The terminal runs a pseudo base station identification algorithm according to the feature data of the target cell, and obtains a confidence level, where the confidence level is used to indicate that the base station of the target cell is a trusted base station, and the pseudo base station identification algorithm is determined by a machine. Learning algorithm training produced;

When the confidence is greater than or equal to the first confidence threshold, the terminal determines that the base station of the target cell is a pseudo base station.
The method of claim 1 wherein

After the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and the confidence is obtained, the method further includes:

When the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal determines The base station of the target cell is a pseudo base station.
The method of claim 2 wherein:

When the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal Determining that the base station of the target cell is a pseudo base station, including:

When the confidence is less than the first confidence threshold and the confidence is greater than or equal to the second confidence threshold, and the terminal detects that the target cell meets the first preset condition, the terminal saves the location Characteristic data of the target cell, the feature data of the target cell is identified as pseudo base station data.
The method of claim 3 wherein:

The terminal saves feature data of the target cell, including:

When the pseudo base station data saving function is enabled, the terminal saves feature data of the target cell; or

When the target cell satisfies the pseudo base station data retention rule, the terminal saves the feature data of the target cell; or

When the pseudo base station data saving function is enabled, and the target cell satisfies the pseudo base station data storage rule, the terminal saves the feature data of the target cell.
A method according to any of claims 2-4, characterized in that

The first preset condition includes at least one of the following conditions:

The terminal intercepts a problem message sent by the target cell;

The terminal is rejected when initiating a location area update request to the target cell;

The terminal is rejected when initiating a service request to the target cell;

The terminal loses the target cell signal within a preset time; or

The location area code LAC of the target cell changes.
A method according to any one of claims 1 to 5, wherein

After the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and obtains the confidence level, the The method also includes:

When the confidence is less than or equal to a fourth confidence threshold, the terminal performs a preset operation by using the target cell, where the preset operation includes network registration, location area update, cell camping, and initiation of a service request. Any of them.
The method of claim 1 wherein

After the terminal runs the pseudo base station identification algorithm according to the feature data of the target cell, and the confidence is obtained, the method further includes:

When the confidence is greater than a fourth confidence threshold and the confidence is less than or equal to a third confidence threshold, and the terminal detects that the target cell meets a second preset condition, the terminal determines the target The base station of the cell is a true base station.
The method of claim 7 wherein:

When the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to a third confidence threshold, and the terminal detects that the target cell meets a second preset condition, the terminal Determining that the base station of the target cell is a true base station, including:

When the confidence level is greater than the fourth confidence threshold and the confidence is less than or equal to the third confidence threshold, and the terminal detects that the target cell meets the second preset condition, the terminal saves the location Characteristic data of the target cell, the feature data of the target cell is identified as true base station data.
The method of claim 8 wherein:

The terminal saves feature data of the target cell, including:

When the true base station data saving function is enabled, the terminal saves feature data of the target cell; or

When the target cell satisfies the true base station data retention rule, the terminal saves the feature data of the target cell; or

When the true base station data saving function is enabled, and the target cell satisfies the true base station data saving rule, the terminal saves the feature data of the target cell.
A method according to any one of claims 7-9, characterized in that

The second preset condition includes at least one of the following conditions:

The terminal establishes a call or data service in the target cell;

The terminal completes the authentication by using the target cell and enters an encryption security mode; or

The terminal completes handover at the target cell.
A method according to any one of claims 1 to 10, wherein

The target cell is a Global System for Mobile Communications (GSM) cell.
A method according to any one of claims 1-11, wherein

The terminal runs a pseudo base station identification algorithm according to the feature data of the target cell, and obtains a confidence level, including:

When the terminal detects that the LAC of the target cell is different from the currently saved LAC, the terminal runs a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level.
Method according to any of claims 1-12, characterized in that

After the terminal determines that the base station of the target cell is a pseudo base station, the method further includes:

The terminal prohibits selecting the target cell again within a preset time period.
A method according to any one of claims 1 to 13, wherein

The feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information.
A terminal, comprising:

a selection unit for selecting a target cell;

An operation unit, configured to run a pseudo base station identification algorithm according to the feature data of the target cell, to obtain a confidence level, where the confidence level is used to indicate that the base station of the target cell is a trusted base station, and the pseudo base station identification algorithm Generated by machine learning algorithm training;

And a determining unit, configured to determine, when the confidence level is greater than or equal to the first confidence threshold, that the base station of the target cell is a pseudo base station.
The terminal of claim 15 wherein:

The determining unit is further configured to: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to a second confidence threshold, and the terminal detects that the target cell meets the first preset When the condition is met, the base station of the target cell is determined to be a pseudo base station.
The terminal of claim 16 wherein:

The determining unit includes a saving module;

The saving module is configured to: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to a second confidence threshold, and the terminal detects that the target cell meets a first preset condition And storing feature data of the target cell, where the feature data of the target cell is identified as pseudo base station data.
The terminal according to claim 17, wherein

The saving module is further configured to: when the pseudo base station data saving function is enabled, save the feature data of the target cell; or, when the target cell satisfies the pseudo base station data saving rule, save the feature data of the target cell; Or, when the pseudo base station data saving function is enabled, and the target cell satisfies the pseudo base station data saving rule, the feature data of the target cell is saved.
A terminal according to any one of claims 16-18, characterized in that

The first preset condition includes at least one of the following conditions:

The terminal intercepts a problem message sent by the target cell;

The terminal is rejected when initiating a location area update request to the target cell;

The terminal is rejected when initiating a service request to the target cell;

The terminal loses the target cell signal within a preset time; or

The location area code LAC of the target cell changes.
A terminal according to any one of claims 15 to 19, characterized in that

The terminal further includes:

An execution unit, configured to perform a preset operation by using the target cell when the confidence is less than or equal to a fourth confidence threshold, where the preset operation includes network registration, location area update, cell camping, and initiation Any of the business requests.
The terminal of claim 15 wherein:

The determining unit is further configured to: when the confidence is greater than a fourth confidence threshold and the confidence is less than or equal to a third confidence threshold, and the terminal detects that the target cell meets a second preset condition Determining that the base station of the target cell is a true base station.
The terminal according to claim 21, characterized in that

The determining unit includes a saving module;

The saving module is configured to: when the confidence level is greater than the fourth confidence threshold, and the confidence is less than or equal to a third confidence threshold, and the terminal detects that the target cell meets a second preset condition And storing feature data of the target cell, where the feature data of the target cell is identified as true base station data.
The terminal according to claim 22, characterized in that

The saving module is further configured to: save the feature data of the target cell when the true base station data saving function is enabled; or save the feature data of the target cell when the target cell satisfies the true base station data saving rule; or And when the true base station data saving function is enabled, and the target cell satisfies the true base station data saving rule, the feature data of the target cell is saved.
A terminal according to any one of claims 21 to 23, characterized in that

The second preset condition includes at least one of the following conditions:

The terminal establishes a call or data service in the target cell;

The terminal completes the authentication by using the target cell and enters an encryption security mode; or

The terminal completes handover at the target cell.
A terminal according to any one of claims 15 to 24, characterized in that

The target cell is a Global System for Mobile Communications (GSM) cell.
A terminal according to any one of claims 15 to 25, characterized in that

The operating unit is further configured to: when the terminal detects that the LAC of the target cell is different from the currently saved LAC, run the pseudo base station identification algorithm according to the feature data of the target cell, to obtain a confidence level.
A terminal according to any one of claims 15 to 26, characterized in that

After the terminal determines that the base station of the target cell is a pseudo base station, the selecting unit is further configured to prohibit the target cell from being selected again within a preset time period.
A terminal according to any one of claims 15 to 27, characterized in that

The feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information.
A terminal, comprising:

Processor and memory;

The processor is arranged to cause the terminal to perform the method of any of claims 1-14 by invoking an operational instruction stored by the memory.
A chip device, characterized in that the device comprises a processing unit;

The processing unit is configured to perform the method according to any one of claims 1-14.
A chip device, comprising:

Processor and memory;

The memory includes instructions that the processor runs to:

Select the target cell;

Performing a pseudo base station identification algorithm according to the feature data of the target cell to obtain a confidence level, where the confidence level is used to indicate that the base station of the target cell is a trusted base station, and the pseudo base station identification algorithm is trained by a machine learning algorithm. Produced

When the confidence is greater than or equal to the first confidence threshold, determining that the base station of the target cell is a pseudo base station.
The chip device according to claim 31, wherein

The processor is further configured to: when the confidence is less than the first confidence threshold and the confidence is greater than or equal to a second confidence threshold, and detecting that the target cell meets a first preset condition, Determining that the base station of the target cell is a pseudo base station.
A chip device according to claim 32, wherein

The processor is further configured to: when the confidence level is less than the first confidence threshold and the confidence is greater than or equal to a second confidence threshold, and detecting that the target cell meets a first preset condition And storing feature data of the target cell, where the feature data of the target cell is identified as pseudo base station data.
A chip device according to claim 33, wherein

The processor saves feature data of the target cell, including:

When the pseudo base station data saving function is enabled, the processor saves feature data of the target cell; or

When the target cell satisfies the pseudo base station data saving rule, the processor saves the feature data of the target cell; or

When the pseudo base station data saving function is enabled, and the target cell satisfies the pseudo base station data saving rule, the processor saves the feature data of the target cell.
A chip device according to any one of claims 32 to 34, characterized in that

The first preset condition includes at least one of the following conditions:

The processor intercepts a problem message sent by the target cell;

The processor is rejected when initiating a location area update request to the target cell;

The processor is rejected when initiating a service request to the target cell;

The processor loses the target cell signal within a preset time; or

The location area code LAC of the target cell changes.
A chip device according to any one of claims 31 to 35, wherein

The processor is further configured to perform a preset operation by using the target cell when the confidence is less than or equal to a fourth confidence threshold, where the preset operation includes network registration, location area update, cell camping, and Initiate any of the business requests.
The chip device according to claim 31, wherein

The processor is further configured to: when the confidence is greater than a fourth confidence threshold and the confidence is less than or equal to a third confidence threshold, and the target cell is detected to meet a second preset condition, determine The base station of the target cell is a true base station.
A chip device according to claim 37, wherein

The processor is further configured to: when the confidence is greater than the fourth confidence threshold and the confidence is less than or equal to a third confidence threshold, and detecting that the target cell meets a second preset condition, The feature data of the target cell is saved, and the feature data of the target cell is identified as true base station data.
A chip device according to claim 38, wherein

The processor is further configured to:

When the true base station data saving function is enabled, the feature data of the target cell is saved; or

Saving the feature data of the target cell when the target cell satisfies the true base station data retention rule; or

When the true base station data saving function is enabled, and the target cell satisfies the true base station data storage rule, the feature data of the target cell is saved.
A chip device according to any one of claims 37 to 39, characterized in that

The second preset condition includes at least one of the following conditions:

The processor establishes a call or data service in the target cell;

The processor completes authentication through the target cell and enters an encryption security mode; or

The processor completes the handover at the target cell.
A chip device according to any one of claims 31 to 40, characterized in that

The processor is further configured to: when detecting that the LAC of the target cell is different from the currently saved LAC, run a pseudo base station identification algorithm according to the feature data of the target cell, to obtain a confidence level.
A chip device according to any one of claims 31 to 41, wherein

The processor is further configured to, after determining that the base station of the target cell is a pseudo base station, prohibit to select the target cell again within a preset time period.
A chip device according to any one of claims 31 to 42, wherein

The processor is further configured to prohibit the target cell from being selected again within a preset duration.
A chip device according to any of claims 31-43, characterized in that

The feature data includes at least one of cell selection and cell reselection information, networking information, service function information, area information, and time information.
A computer program comprising instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-14.
A computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1-14.
A computer program product comprising instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1-14.