CN114338915A - Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium - Google Patents
Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium Download PDFInfo
- Publication number
- CN114338915A CN114338915A CN202111605148.4A CN202111605148A CN114338915A CN 114338915 A CN114338915 A CN 114338915A CN 202111605148 A CN202111605148 A CN 202111605148A CN 114338915 A CN114338915 A CN 114338915A
- Authority
- CN
- China
- Prior art keywords
- risk
- incoming call
- preset
- early warning
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000012502 risk assessment Methods 0.000 claims abstract description 23
- 238000013210 evaluation model Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 12
- 230000011664 signaling Effects 0.000 claims description 8
- 238000013461 design Methods 0.000 description 19
- 238000007726 management method Methods 0.000 description 15
- 230000000875 corresponding effect Effects 0.000 description 14
- 238000012544 monitoring process Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003340 mental effect Effects 0.000 description 2
- 230000004630 mental health Effects 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Images
Abstract
The application provides a method, a device, equipment and a storage medium for identifying a risk of an incoming call number. The method comprises the steps of firstly obtaining an incoming call number of a monitored object from returned data of a preset acquisition module, then determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library, if so, determining a risk value of the risk early warning number according to a preset risk evaluation model, and carrying out risk decision on the risk early warning number according to the risk value. Whether the incoming call number is a risk early warning number is determined based on a reference number in a local number library, when the incoming call number is the risk early warning number, a risk value of the risk early warning number is determined according to a preset risk assessment model, the risk value is adopted to quantify the possible telephone fraud risk of the risk early warning number, then the risk value of the risk early warning number is utilized to carry out risk decision, the occurrence that a monitored object suffers telephone fraud is reduced, and a safer and more reliable living environment is provided for the monitored object.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a storage medium for identifying a risk of an incoming call number.
Background
With the continuous popularization of networks and the rapid development of information technologies, various internet applications bring convenience to life, study and work of people.
However, the science and technology is to layer various new forms of telephone fraud crime with the help of the east wind of the information technology and the communication network for the double-edged sword, so that telephone fraud incidents often occur, and particularly, groups such as students and old people with weak awareness and weak self-protection capability for telephone fraud become a deceived serious disaster area. The occurrence of telephone fraud poses various degrees of serious harm to the physical and mental health and property safety of the deceived parties.
Therefore, a risk identification scheme for incoming call numbers is needed to reduce the occurrence of phone fraud.
Disclosure of Invention
The application provides an incoming call number risk identification method, an incoming call number risk identification device, incoming call number risk identification equipment and a storage medium, which are used for providing an incoming call number risk identification scheme and reducing telephone fraud.
In a first aspect, the present application provides a method for identifying a risk of an incoming call number, including:
acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module;
determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library;
if yes, determining a risk value of the risk early warning number according to a preset risk assessment model, and performing risk decision on the risk early warning number according to the risk value.
In one possible design, the determining whether the incoming call number is a risk early warning number according to a preset matching policy and a reference number in a local number library includes:
matching the incoming call number with a reference number in a local number library, wherein the local number library comprises a white list number list, a visitor number list, a black list number list and a cache number list;
if the incoming call number is a reference number in the white list number list or the visitor number list, determining that the incoming call number is a non-risk early warning number;
if the incoming call number is a strange number, or the incoming call number is a reference number in the blacklist number table or the cache number table, determining that the incoming call number is the risk early warning number;
the strange number refers to any calling number except the reference number of the local number library.
In one possible design, before the matching the incoming call number with the reference number in the local number library, the method further includes:
acquiring user characteristic information of the monitored object through a campus management platform, wherein the user characteristic information comprises user identity information, user family information, user academic information and user number information;
determining reference numbers in the white list number list, the visitor number list and the black list number list according to the user characteristic information;
the method comprises the steps of obtaining a real-time number and marking information from a cloud data platform, determining a reference number in a cache number table according to the real-time number and the marking information, wherein the marking information is used for marking the incoming intention attribute of the real-time number.
In one possible design, the determining the risk value of the risk pre-warning number according to a preset risk assessment model includes:
counting the first incoming call times and interval time of the risk early warning number within a first preset time, wherein the interval time is a difference value between the first incoming call time and the current time of the risk early warning number;
and inputting the first incoming call times, the interval duration and a preset dynamic factor into the preset risk assessment model, and determining an obtained output result as a risk value of the risk early warning number.
In one possible design, before the inputting the first number of incoming calls, the interval duration, and a preset dynamic factor into the preset risk assessment model, the method further includes:
and generating the preset dynamic factor according to a preset entropy value, wherein the preset dynamic factor is in positive correlation with the preset entropy value, and the preset entropy value is used for representing the occurrence rate of the telephone fraud phenomenon.
In one possible design, the making a risk decision for the risk pre-warning number according to the risk value includes:
recording the risk value of the risk early warning number, and judging whether the risk value of the risk early warning number exceeds a preset risk value or not;
if yes, determining the risk early warning number as a risk number, and storing the risk number into the blacklist number table;
acquiring second incoming call times of the risk number within a second preset time length, and judging whether the second incoming call times exceed preset times;
and if so, generating reminding information and sending the reminding information to the preparation terminal.
In a possible design, before the obtaining the incoming call number of the monitored object from the return data of the preset acquisition module, the method further includes:
analyzing the call signaling of the core network element in real time through the preset acquisition module to obtain analysis data, wherein the preset acquisition module supports different operators and different network standards;
and encrypting the analysis data by using a preset encryption model to obtain the return data of the preset acquisition module.
In a second aspect, the present application provides an incoming call number risk identification device, including:
the acquisition module is used for acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module;
the first processing module is used for determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library;
and the second processing module is used for determining the risk value of the risk early warning number according to a preset risk evaluation model and carrying out risk decision on the risk early warning number according to the risk value if the risk value is positive.
In one possible design, the first processing module is specifically configured to:
matching the incoming call number with a reference number in a local number library, wherein the local number library comprises a white list number list, a visitor number list, a black list number list and a cache number list;
if the incoming call number is a reference number in the white list number list or the visitor number list, determining that the incoming call number is a non-risk early warning number;
if the incoming call number is a strange number, or the incoming call number is a reference number in the blacklist number table or the cache number table, determining that the incoming call number is the risk early warning number;
the strange number refers to any calling number except the reference number of the local number library.
In one possible design, the device for identifying risk of incoming call number further includes: a third processing module; the third processing module is configured to:
acquiring user characteristic information of the monitored object through a campus management platform, wherein the user characteristic information comprises user identity information, user family information, user academic information and user number information;
determining reference numbers in the white list number list, the visitor number list and the black list number list according to the user characteristic information;
the method comprises the steps of obtaining a real-time number and marking information from a cloud data platform, determining a reference number in a cache number table according to the real-time number and the marking information, wherein the marking information is used for marking the incoming intention attribute of the real-time number.
In one possible design, the second processing module is specifically configured to:
counting the first incoming call times and interval time of the risk early warning number within a first preset time, wherein the interval time is a difference value between the first incoming call time and the current time of the risk early warning number;
and inputting the first incoming call times, the interval duration and a preset dynamic factor into the preset risk assessment model, and determining an obtained output result as a risk value of the risk early warning number.
In one possible design, the second processing module is further configured to:
and generating the preset dynamic factor according to a preset entropy value, wherein the preset dynamic factor is in positive correlation with the preset entropy value, and the preset entropy value is used for representing the occurrence rate of the telephone fraud phenomenon.
In one possible design, the second processing module is further configured to:
recording the risk value of the risk early warning number, and judging whether the risk value of the risk early warning number exceeds a preset risk value or not;
if yes, determining the risk early warning number as a risk number, and storing the risk number into the blacklist number table;
acquiring second incoming call times of the risk number within a second preset time length, and judging whether the second incoming call times exceed preset times;
and if so, generating reminding information and sending the reminding information to the preparation terminal.
In one possible design, the device for identifying risk of incoming call number further includes: a fourth processing module; the fourth processing module is configured to:
analyzing the call signaling of the core network element in real time through the preset acquisition module to obtain analysis data, wherein the preset acquisition module supports different operators and different network standards;
and encrypting the analysis data by using a preset encryption model to obtain the return data of the preset acquisition module.
In a third aspect, the present application provides an electronic device, comprising: a processor; and the number of the first and second groups,
a memory for storing a computer program;
wherein the processor is configured to execute any one of the possible incoming call number risk identification methods provided by the first aspect via execution of the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, where the computer program is used to execute any one of the possible incoming call number risk identification methods provided in the first aspect.
In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements any one of the possible incoming call number risk identification methods provided in the first aspect.
The application provides a method, a device, equipment and a storage medium for identifying a risk of an incoming call number. The method comprises the steps of firstly obtaining an incoming call number of a monitored object from returned data of a preset acquisition module, then determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library, if so, determining a risk value of the risk early warning number according to a preset risk evaluation model, and carrying out risk decision on the risk early warning number according to the risk value. Whether the incoming call number is a risk early warning number is determined based on a reference number in a local number library, when the incoming call number is the risk early warning number, a risk value of the risk early warning number is determined according to a preset risk assessment model, the risk degree of the risk early warning number is quantified by the risk value, then a risk decision is made by the risk value of the risk early warning number, the occurrence of telephone fraud of a monitored object is reduced, and a safer and more reliable living environment is provided for the monitored object.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for identifying a risk of an incoming call number according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another incoming call number risk identification method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another method for identifying a risk of an incoming call number according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another incoming call number risk identification method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an incoming call number risk identification device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of another incoming call number risk identification device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of methods and apparatus consistent with certain aspects of the present application, as detailed in the appended claims.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings (if any) are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
At present, various novel forms of telephone fraud crimes emerge endlessly, so that telephone fraud events often occur, particularly, groups such as students and old people with weak awareness and weak self-protection capability for preventing telephone fraud become cheated disaster areas, and the occurrence of telephone fraud causes serious harm to physical and mental health and property safety of cheated parties in different degrees. Therefore, in order to provide a safer and more reliable learning and living environment for people, a risk identification scheme for incoming numbers of mobile phones is needed to reduce the occurrence of phone fraud.
In view of the above problems in the prior art, the present application provides a method, an apparatus, a device and a storage medium for identifying a risk of an incoming call number. The invention conception of the incoming call number risk identification method provided by the application is as follows: the incoming call number of the monitored object is obtained from the returned data of the preset acquisition module, the monitored object is a target group in a monitored area, for example, the monitored area is a campus, the monitored object can be students in study and life in the campus, then a reference number in a local number library is determined through accessing a corresponding management platform and a cloud data platform which can obtain user characteristic information of the monitored object in the monitored area, whether the incoming call number is a risk early warning number is further determined based on a preset matching strategy and the reference number in the local number library, the risk early warning number is the incoming call number with possible fraud risk, a risk value of the risk early warning number is determined according to a preset risk evaluation model so as to quantify the possible telephone fraud risk of the incoming call number, finally, a risk decision is made for the risk early warning number according to the risk value, and the occurrence of telephone fraud is reduced, provides a safer and more reliable learning and living environment.
An exemplary application scenario of the embodiments of the present application is described below.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, as shown in fig. 1, a first terminal device 11 is a user terminal of a monitoring object, and a processor in a second terminal device 12 is configured to execute an incoming call number risk identification method provided in the embodiment of the present application, so as to perform risk identification on an incoming call number dialed into the first terminal device 11, for example, first obtain the incoming call number dialed into the first terminal device 11, then determine whether the incoming call number is a risk early warning number according to a preset matching policy and a reference number in a local number library, if so, determine a risk value of the risk early warning number according to a preset risk evaluation model, quantify a risk of telephone fraud that the incoming call number may have by using the risk value, and further perform risk decision on the risk early warning number according to the risk value, thereby reducing occurrence of telephone fraud.
The first terminal device 11 may be a mobile communication device such as a mobile phone and a smart watch, and the second terminal device 12 may be a computer, a server cluster, and other corresponding devices that can execute the incoming call number risk identification method provided in the embodiment of the present application. In the embodiment of the present application, specific types of the first terminal device 11 and the second terminal device 12 are not limited, in fig. 1, the first terminal device 11 is illustrated by taking a mobile phone as an example, and the second terminal device 12 is illustrated by taking a computer as an example.
It should be noted that the above application scenarios are only exemplary, and the method, the apparatus, the device, and the storage medium for identifying risk of incoming call number provided in the embodiment of the present application include, but are not limited to, the above application scenarios.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart illustrating a method for identifying a risk of an incoming call number according to an embodiment of the present application. As shown in fig. 2, the embodiment of the present application includes:
s101: and acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module.
For example, a preset acquisition module capable of supporting different operators and different network standards is configured first, and a corresponding acquisition policy may be configured in advance in the preset acquisition module, so that the preset acquisition module may analyze a call signaling of a core network element in real time to obtain corresponding analysis data. The preset acquisition module is used for acquiring the calling signaling to acquire the incoming call number of the monitored object.
The call signaling to be analyzed is, for example, real-time call information from a campus teacher, so that the obtained analysis data includes an incoming call number to a campus student, and the campus student is the monitoring object in the embodiment of the present application. It is to be understood that the monitored objects include, but are not limited to, campus students, which may be any target group within the monitored area. And correspondingly, the call signaling is the real-time call information of the target group in the monitoring area.
After the analysis data is obtained, for confidentiality and security, the analysis data can be encrypted by using a preset encryption model, and then the encrypted analysis data is transmitted back to the corresponding electronic equipment capable of executing the incoming call number risk identification method provided by the embodiment of the application, so that the transmitted back data of the preset acquisition module can be obtained.
And further decrypting the returned data by adopting a decryption model corresponding to the preset encryption model so as to acquire the incoming call number of the monitored object from the returned data of the preset acquisition module.
S102: and determining whether the incoming call number is a risk early warning number according to a preset matching strategy and the reference number in the local number library.
And after the incoming call number of the monitored object is acquired, judging whether the incoming call number is a risk early warning number according to the reference number in the local number library and a preset matching strategy set in advance. If the incoming call number is determined to be the risk early warning number, it indicates that there is a possible risk of phone fraud for the incoming call number, and step S103 is further performed. On the contrary, if the incoming call number is determined to be not the risk early warning number, the incoming call number is indicated to have no phone fraud risk, and only the incoming call number is recorded.
For example, the reference number in the local number library may be maintained in advance, for example, a number library, i.e., a local number library, is maintained locally in advance, and the telephone number in the local number library is the reference number, for example, the local number library may include a white list number table, a visitor number table, a black list number table, and a cache number table. And determining whether the incoming call number is a risk early warning number according to a matching result by matching the incoming call number with a reference number in a local number library, wherein the preset matching strategy comprises a matching mode and a determination mode for determining whether the incoming call number is the risk early warning number according to the matching result.
In a possible design, assuming that the monitoring area is a campus and the management platform for obtaining the user characteristic information of the monitored object is a campus management platform, before the incoming number is matched with the reference number in the local number library, the method may further include a step of determining the reference number in the local number library as shown in fig. 3. Fig. 3 is a flowchart illustrating another incoming call number risk identification method according to an embodiment of the present application. As shown in fig. 3, the embodiment of the present application includes:
s201: and acquiring the user characteristic information of the monitored object through the campus management platform.
The user characteristic information comprises user identity information, user family information, user academic information and user number information.
S202: and determining reference numbers in the white list number list, the visitor number list and the black list number list according to the user characteristic information.
And acquiring the user characteristic information of the monitored object by butting with the campus management platform. The campus management platform is a corresponding management platform operated by a school to manage student information. User characteristic information, e.g. user identity information, such as age, name, gender, occupation, habits, etc. User family information such as emergency contacts, parents, etc. of the monitored object, user academic information may be, for example, user grade information, etc. The subscriber number information may be, for example, information that the monitoring object actively uses to mark the incoming number.
It should be noted that the specific content included in the user characteristic information may be adaptively set according to the specific group of the monitoring object, and the foregoing embodiment is only an illustrative example, and is not a limitation on the specific content of the user characteristic information.
After the user characteristic information of the monitored object is obtained, reference numbers in a white list number list, a visitor number list and a black list number list are determined according to the user characteristic information.
For example, the telephone number of a person associated with the monitored object may be determined as a reference number in the white list number table, related persons such as parents, teachers, classmates, etc. of the monitored object, such persons are familiar with the relationship network of the monitored object, the telephone number of such persons is updated frequently and has a high security level, and thus, the telephone number of such persons may be determined as a reference number in the white list number table. In addition, in order to ensure the security of the white list number table, corresponding management permissions can be set for the reference numbers in the white list number table, for example, only managers with related permissions are allowed to locally maintain the reference numbers in the white list number table, for example, if the reference numbers need to be changed, the reference numbers need to be audited by the managers and are confirmed with the monitored object, and the reference numbers can be changed when the audit and the confirmation are successful, so that the reference numbers are prevented from being shared with the outside.
The reference number in the visitor number table is a corresponding incoming call number, such as a telephone number for express delivery, takeaway, and the like, which is received by the monitoring object and is actively marked as a non-risk early warning number at the user terminal, so as to prevent the monitoring object from being influenced by the risk early warning number determined when the incoming call number is called again.
The reference number in the blacklist number list is the corresponding incoming call number which is received by the monitoring object and is actively marked as a risk early warning number at the user terminal.
As can be seen from the above description, the telephone number in the white list number table may be determined according to at least one of the user identity information, the user family information, and the user academic information in the user feature information, and the reference number in the visitor number table and the reference number in the black list number table may be determined according to the user number information in the user feature information.
In addition, the corresponding management strategy can be set for the reference number in the local number library so as to implement customized management for the incoming call number of the monitored object. For example, according to the user characteristic information, all incoming calls are allowed for a high-grade and security-conscious monitoring object, and the call records can not be counted in full. And according to the monitoring of the user characteristic information on the lack of low-grade and safety consciousness, the incoming calls of unknown incoming call numbers are effectively intercepted by setting labels such as 'only white list number list can be used for incoming calls', so that the risk of fraud of the monitored object is reduced.
It should be noted that the customized management may be adaptively configured according to a specific group of monitoring objects, and the above description is only an illustrative example.
S203: and acquiring the real-time number and the marking information from the cloud data platform, and determining the reference number in the cache number table according to the real-time number and the marking information.
The marking information is used for marking the incoming call intention attribute of the real-time number.
The method comprises the steps of connecting a cloud data platform, obtaining a real-time number and marking information from the cloud data platform, wherein the marking information can be information capable of marking incoming call intention attributes of the real-time number, such as company name, related attributes (such as express delivery, takeaway and sales promotion), safety level and the like. The information sources of the real-time numbers in the cloud data platform include but are not limited to departments, commercial institutions and the like in related fields such as mobile phone communication and information security and the like.
The real-time numbers acquired by the cloud data platform belong to online real-time data, the cloud data platform can realize low-delay query and processing efficiency in a high-concurrency scene, and meanwhile, the query and identification accuracy is guaranteed.
After the real-time number and the marking information are acquired from the cloud data platform, the acquired real-time number and the corresponding marking information can be subjected to data cleaning, integration and other processing, then the processed real-time number is determined to be a reference number in a cache number table, and the reference number carries the marking information to represent the incoming call intention attribute.
The reference number in the cache number table is used for ensuring the accurate matching and identification of the incoming call number of the monitored object when the offline state or the network state is not good.
According to the incoming call number risk identification method provided by the embodiment of the application, before the incoming call number is matched with the reference number in the local number library, the reference number in the local number library is determined through butt joint with the campus management platform and the cloud data platform, and whether the incoming call number is a risk early warning number or not is judged through the reference number in the local number library. The accuracy and the real-time degree of determining whether the incoming call number is a risk early warning number by using the reference number in the local number library can be improved by determining the reference number in each of the white list number list, the visitor number list, the black list number list and the cache number list in the local number library, and the feasibility and the applicability of the risk identification of the incoming call number are improved.
S103: if yes, determining a risk value of the risk early warning number according to a preset risk evaluation model, and performing risk decision on the risk early warning number according to the risk value.
If the incoming call number is determined to be a risk early warning number according to the preset matching strategy and the reference number in the local number library, the risk early warning number can be subjected to statistical recording, the risk value of the risk early warning number is further determined according to a preset risk evaluation model, so that the possible telephone fraud risk of the incoming call number is quantified, for example, the risk value is output and recorded, and then risk decision is carried out on the risk early warning number according to the risk value.
In a possible design, a possible implementation manner of performing risk decision on the risk early warning number according to the risk value in step S103 is shown in fig. 4. Fig. 4 is a schematic flowchart of another incoming call number risk identification method provided in the embodiment of the present application, and as shown in fig. 4, the embodiment of the present application includes:
s301: and recording the risk value of the risk early warning number, and judging whether the risk value of the risk early warning number exceeds a preset risk value.
S302: if yes, determining the risk early warning number as a risk number, and storing the risk number into a blacklist number list.
For example, the risk value of the risk early warning number determined according to the preset risk assessment model is output and recorded, and then the risk value of the risk early warning number is compared with the preset risk value, and whether the risk value of the risk early warning number exceeds the preset risk value is judged. If the number exceeds the preset risk value, namely the risk value is greater than or equal to the preset risk value, the risk early warning number is determined as a risk number, the telephone fraud risk existing in the risk early warning number is relatively high, and the risk number is stored in a blacklist number table to be used as a reference number in the blacklist number table. Otherwise, if the risk value does not exceed the preset risk value, only the risk early warning number is counted and recorded.
The specific value of the preset risk value can be set according to the actual situation, and the embodiment of the application is not limited.
S303: and acquiring a second incoming call frequency of the risk number within a second preset time length, and judging whether the second incoming call frequency exceeds the preset frequency.
S304: if yes, generating reminding information and sending the reminding information to the preparation terminal.
Further, the second incoming call frequency of the risk number within the second preset duration may be obtained, in other words, the incoming call frequency of the risk number within the second preset duration is counted, whether the second incoming call frequency is greater than or equal to (i.e., exceeds) the preset frequency is judged, if the second incoming call frequency exceeds the preset frequency, the reminding information is generated, and the reminding information is sent to the preparation terminal, where the preparation terminal may be, for example, a mobile terminal of a contact such as a parent, a teacher, a relative of the monitored object, or a contact terminal of a government organization to which the campus belongs, so as to perform multiparty supervision on the monitored object, and avoid occurrence of telephone fraud on the monitored object.
The specific values of the second preset duration and the preset times can be set according to actual conditions, and the embodiment of the application is not limited. The reminding information is short message, notification information and the like.
According to the incoming call number risk identification method provided by the embodiment of the application, firstly, the incoming call number of a monitored object is obtained from the returned data of the preset acquisition module, then whether the incoming call number is a risk early warning number or not is determined according to the preset matching strategy and the reference number in the local number library, if yes, the risk value of the risk early warning number is determined according to the preset risk assessment model, the risk value is recorded, and risk decision is carried out on the risk early warning number according to the risk value. Whether the incoming call number is a risk early warning number is determined based on a reference number in a local number library, when the incoming call number is the risk early warning number, a risk value of the risk early warning number is determined according to a preset risk assessment model, the risk degree of the risk early warning number is quantified by the risk value, then a risk decision is made by the risk value of the risk early warning number, the incidence rate of telephone fraud of a monitored object is reduced, and a safer and more reliable living environment is provided for the monitored object.
On the basis of the foregoing embodiment, fig. 5 is a flowchart illustrating a further method for identifying risk of incoming call number according to the embodiment of the present application. As shown in fig. 5, the embodiment of the present application includes:
s401: and acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module.
The implementation manner, principle and effect of step S401 are similar to those of step S101, and the detailed process can refer to the foregoing description, and is not repeated herein.
S402: the incoming number is matched to a reference number in a local number repository.
The local number library comprises a white list number list, a visitor number list, a black list number list and a cache number list.
S403 a: and if the incoming call number is a reference number in a white list number list or a visitor number list, determining that the incoming call number is a non-risk early warning number.
S403 b: and if the incoming call number is a strange number, or the incoming call number is a reference number in a blacklist number table or a cache number table, determining that the incoming call number is a risk early warning number.
And matching the incoming call number with a reference number in a local number library, if the incoming call number is a reference number in a white list number table or a visitor number table in the local number library, determining that the incoming call number is a non-risk early warning number, and only recording the incoming call number. And if the matching is carried out, the incoming call number is a strange number, or the incoming call number is a reference number in a blacklist number table or a cache number table, determining the incoming call number as a risk early warning number, and further executing the step S404a and the step S404 b. The strange number is a number unknown to the local number library, namely, any calling number except for the reference number of the local number library is not matched with any reference number in the local number library.
The incoming call number is compared with the reference number in the local number library, so that whether the incoming call number is a risk early warning number or not is judged, and the incoming call number with the possible telephone fraud risk is preliminarily identified.
S404 a: and counting the first incoming call times and the interval time of the risk early warning number within the first preset time.
The interval duration refers to a difference value between the first incoming call time of the risk early warning number and the current time.
S404 b: and inputting the first incoming call times, the interval duration and the preset dynamic factor into a preset risk evaluation model, and determining the obtained output result as a risk value of the risk early warning number.
And counting the incoming call times of the incoming call number which is judged as the risk early warning number within the first preset time length and the difference between the first incoming call time and the current time of the incoming call number. The first incoming call frequency refers to the incoming call frequency of the risk early warning number in a first preset time period, and the interval time period refers to the difference between the first incoming call time and the current time of the risk early warning number.
And further inputting the first incoming call times, the interval duration and the preset dynamic factor into a preset risk evaluation model to obtain an output result, wherein the output result is a risk value of the risk early warning number. The preset risk assessment model is a calculation model for calculating a risk value through the first power frequency, the interval duration and the preset dynamic factor, and is, for example, as shown in the following formula (1):
wherein N represents the first incoming call times, T represents the interval duration, d represents a preset dynamic factor, and R represents a risk value.
The preset risk assessment model shown in formula (1) is formed based on the following considerations:
first, if the incoming call number has more incoming calls, it indicates that the incoming call number is more likely to be a telephone fraud number, and thus N is set in proportion to R;
secondly, considering that the early stage of the group call of the fraud phone to the number of the monitored object, the incoming call number and the telephone operation of the monitored object to the phone fraud are in strange stages, so that the protection against the fraud is relatively weak, and the fraud is most easily caused, so that the risk value at this time is very large. And as time goes on, the cognition of the monitoring object on the incoming call number and the telephone operation of the telephone fraud is gradually deepened, the psychology of the telephone fraud is prevented from being enhanced, and the risk value is gradually reduced at this stage. In view of this consideration, T and R in the preset risk assessment model may be set in inverse proportion;
third, the preset dynamic factor may be set based on a phone fraud phenomenon of the external environment. For example, if it is known through media and other approaches that the recent telephone fraud phenomenon of the external environment is frequent, the preset dynamic factor, that is, the value of d, may be appropriately increased, so that the risk value of the risk early warning number is increased, and more risk early warning numbers are determined as risk numbers. On the contrary, if the recent telephone fraud phenomenon of the external environment is less, the value of d is properly reduced, so that the risk value of the risk early warning number is reduced. Therefore, the preset dynamic factor, namely the value of d, determines the change speed of the risk value, and the larger the value of d is, the faster the risk value is reduced.
As can be seen from the description of the third point, the preset dynamic factor, i.e., the value of d, can be set and adjusted according to the occurrence rate of the phone fraud phenomenon in the external environment, so that the preset risk assessment model can be a dynamic risk calculation model.
For example, before inputting the first incoming call number, the interval duration and the preset dynamic factor into the preset risk assessment model, the method further comprises:
and generating a preset dynamic factor according to the preset entropy, wherein the preset dynamic factor is positively correlated with the preset entropy, and the preset entropy is used for representing the occurrence rate of the telephone fraud phenomenon of the external environment. For example, the occurrence rate of the telephone fraud phenomenon of the external environment becomes high, that is, the preset entropy value becomes large, and the preset dynamic factor is adaptively set according to the preset entropy value. When the occurrence rate of the telephone fraud phenomenon of the external environment is reduced, namely the preset entropy value is reduced, the preset dynamic factor is adaptively reset according to the preset entropy value. Therefore, the preset dynamic factor is adjusted in real time, so that the caller ID with phone fraud is exposed as much as possible, and the practicability of the caller ID risk identification method provided by the embodiment of the application is improved.
Based on the above three points, a preset risk assessment model shown in formula (1) can be formed, and the risk value of the risk early warning number is obtained through calculation, so that the telephone fraud risk is quantified. It should be noted that the preset risk assessment model includes, but is not limited to, the model shown in formula (1), and may also be another corresponding calculation model obtained by counting other parameters of the risk early warning number, which is not limited in this embodiment of the present application.
S405: and recording the risk value, and performing risk decision on the risk early warning number according to the risk value.
The implementation manner, principle and effect of step S405 are similar to those of the risk decision-making performed on the risk early warning number according to the risk value in step S103 in the foregoing embodiment, and the detailed process may refer to the foregoing description, which is not repeated herein.
In the implementation mode that the monitored object is a student, most schools may distribute mobile phone cards uniformly for the mobile phone numbers of the students, so that the mobile phone numbers of the students have certain relevance and continuity, and the existence of the characteristic greatly increases the risk of telephone fraud of the student numbers. Therefore, for the problem of the number group being fraudulently in the campus scenario, the method for identifying the incoming call number provided by the embodiment of the application is more necessary to further calculate the risk value of the incoming call number determined as the risk early warning number on the basis of determining whether the incoming call number is the risk early warning number based on the reference number in the local number library. After the incoming call number is determined as the risk early warning number, the risk value of the risk early warning number is further calculated by using the preset risk assessment model, the identification rate of the risk number is effectively improved, the occurrence of telephone fraud of students is greatly reduced, a safer and more reliable learning environment is provided for the students, and more possibilities are provided for campus intelligent services.
Fig. 6 is a schematic structural diagram of an incoming call number risk identification device according to an embodiment of the present application. As shown in fig. 6, an incoming call number risk identification apparatus 500 provided in the embodiment of the present application includes:
an obtaining module 501, configured to obtain an incoming call number of a monitored object from return data of a preset acquisition module;
a first processing module 502, configured to determine whether the incoming call number is a risk early warning number according to a preset matching policy and a reference number in a local number library;
and the second processing module 503 is configured to determine a risk value of the risk early warning number according to the preset risk assessment model if the risk value is positive, and perform risk decision on the risk early warning number according to the risk value.
In one possible design, the first processing module 502 is specifically configured to:
matching the incoming call number with a reference number in a local number library, wherein the local number library comprises a white list number list, a visitor number list, a black list number list and a cache number list;
if the incoming call number is a reference number in a white list number list or a visitor number list, determining that the incoming call number is a non-risk early warning number;
and if the incoming call number is a strange number, or the incoming call number is a reference number in a blacklist number table or a cache number table, determining that the incoming call number is a risk early warning number.
The strange number refers to any calling number except the reference number of the local number library.
Based on fig. 6, fig. 7 is a schematic structural diagram of another incoming call number risk identification device according to an embodiment of the present application. As shown in fig. 7, the device 500 for identifying risk of incoming call number provided in the embodiment of the present application further includes: a third processing module 504, the third processing module 504 configured to:
acquiring user characteristic information of a monitored object through a campus management platform, wherein the user characteristic information comprises user identity information, user family information, user academic information and user number information;
determining reference numbers in a white list number list, a visitor number list and a black list number list according to the user characteristic information;
and acquiring a real-time number and marking information from the cloud data platform, and determining a reference number in the cache number table according to the real-time number and the marking information, wherein the marking information is used for marking the incoming call intention attribute of the real-time number.
In one possible design, the second processing module 503 is specifically configured to:
counting the first incoming call times and interval time of the risk early warning number within a first preset time, wherein the interval time refers to the difference between the first incoming call time and the current time of the risk early warning number;
and inputting the first incoming call times, the interval duration and the preset dynamic factor into a preset risk evaluation model, and determining the obtained output result as a risk value of the risk early warning number.
In one possible design, the second processing module 503 is further configured to:
and generating a preset dynamic factor according to the preset entropy, wherein the preset dynamic factor is in positive correlation with the preset entropy, and the preset entropy is used for representing the occurrence rate of the telephone fraud phenomenon.
In one possible design, the second processing module 503 is further configured to:
recording a risk value of the risk early warning number, and judging whether the risk value of the risk early warning number exceeds a preset risk value or not;
if so, determining the risk early warning number as a risk number, and storing the risk number into a blacklist number list;
acquiring second incoming call times of the risk number within a second preset time length, and judging whether the second incoming call times exceed the preset times;
if yes, generating reminding information and sending the reminding information to the preparation terminal.
In one possible design, the incoming call number risk identification apparatus 500 further includes: and a fourth processing module. The fourth processing module is configured to:
analyzing the call signaling of the core network element in real time through a preset acquisition module to obtain analyzed data, wherein the preset acquisition module supports different operators and different network systems;
and encrypting the analysis data by using a preset encryption model to obtain the return data of the preset acquisition module.
The device for identifying risk of incoming call number provided by the embodiment of the application can execute corresponding steps of the method for identifying risk of incoming call number in the embodiment of the method, and the implementation principle and the technical effect are similar, and are not repeated herein.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 8, the electronic device 600 may include: at least one processor 601 and memory 602. Fig. 9 shows an electronic device as an example of a processor.
A memory 602 for storing programs. In particular, the program may include program code comprising computer-executable instructions.
The memory 602 may comprise high-speed RAM memory, and may also include non-volatile memory (MoM-volatile memory), such as at least one disk memory.
The processor 601 is configured to execute the computer program stored in the memory 602 to implement the incoming call number risk identification method.
The processor 601 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.
Alternatively, the memory 602 may be separate or integrated with the processor 601. When the memory 602 is a device separate from the processor 601, the electronic device 600 may further include:
a bus 603 for connecting the processor 601 and the memory 602. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. Buses may be classified as address buses, data buses, control buses, etc., but do not represent only one bus or type of bus.
Alternatively, in a specific implementation, if the memory 602 and the processor 601 are integrated into a single chip, the memory 602 and the processor 601 may communicate via an internal interface.
The present application also provides a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, are specifically, the computer-readable storage medium stores program instructions, and the program instructions are used in the method for identifying risk of incoming call number in the foregoing embodiment.
The present application further provides a computer program product comprising a computer program, which when executed by a processor implements the method for risk identification of an incoming call number in the above embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for identifying a risk of an incoming call number is characterized by comprising the following steps:
acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module;
determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library;
if yes, determining a risk value of the risk early warning number according to a preset risk assessment model, and performing risk decision on the risk early warning number according to the risk value.
2. The method for identifying risk of incoming call number according to claim 1, wherein the determining whether the incoming call number is a risk early warning number according to a preset matching policy and a reference number in a local number library comprises:
matching the incoming call number with a reference number in a local number library, wherein the local number library comprises a white list number list, a visitor number list, a black list number list and a cache number list;
if the incoming call number is a reference number in the white list number list or the visitor number list, determining that the incoming call number is a non-risk early warning number;
if the incoming call number is a strange number, or the incoming call number is a reference number in the blacklist number table or the cache number table, determining that the incoming call number is the risk early warning number;
the strange number refers to any calling number except the reference number of the local number library.
3. The method according to claim 2, further comprising, before said matching the incoming call number with a reference number in the local number repository:
acquiring user characteristic information of the monitored object through a campus management platform, wherein the user characteristic information comprises user identity information, user family information, user academic information and user number information;
determining reference numbers in the white list number list, the visitor number list and the black list number list according to the user characteristic information;
the method comprises the steps of obtaining a real-time number and marking information from a cloud data platform, determining a reference number in a cache number table according to the real-time number and the marking information, wherein the marking information is used for marking the incoming intention attribute of the real-time number.
4. The method for identifying risk of incoming call number according to claim 3, wherein the determining the risk value of the risk pre-warning number according to a preset risk assessment model comprises:
counting the first incoming call times and interval time of the risk early warning number within a first preset time, wherein the interval time is a difference value between the first incoming call time and the current time of the risk early warning number;
and inputting the first incoming call times, the interval duration and a preset dynamic factor into the preset risk assessment model, and determining an obtained output result as a risk value of the risk early warning number.
5. The method according to claim 4, further comprising, before the inputting the first incoming call number, the interval duration and a preset dynamic factor into the preset risk assessment model:
and generating the preset dynamic factor according to a preset entropy value, wherein the preset dynamic factor is in positive correlation with the preset entropy value, and the preset entropy value is used for representing the occurrence rate of the telephone fraud phenomenon.
6. The method for identifying the risk of the incoming call number according to any one of claims 2 to 5, wherein the risk decision making on the risk pre-warning number according to the risk value comprises:
recording the risk value of the risk early warning number, and judging whether the risk value of the risk early warning number exceeds a preset risk value or not;
if yes, determining the risk early warning number as a risk number, and storing the risk number into the blacklist number table;
acquiring second incoming call times of the risk number within a second preset time length, and judging whether the second incoming call times exceed preset times;
and if so, generating reminding information and sending the reminding information to the preparation terminal.
7. The method for identifying risk of incoming call number according to claim 6, wherein before the step of obtaining the incoming call number of the monitored object from the returned data of the preset collection module, the method further comprises:
analyzing the call signaling of the core network element in real time through the preset acquisition module to obtain analysis data, wherein the preset acquisition module supports different operators and different network standards;
and encrypting the analysis data by using a preset encryption model to obtain the return data of the preset acquisition module.
8. An incoming call number risk identification device, comprising:
the acquisition module is used for acquiring the incoming call number of the monitored object from the returned data of the preset acquisition module;
the first processing module is used for determining whether the incoming call number is a risk early warning number according to a preset matching strategy and a reference number in a local number library;
and the second processing module is used for determining the risk value of the risk early warning number according to a preset risk evaluation model and carrying out risk decision on the risk early warning number according to the risk value if the risk value is positive.
9. An electronic device, comprising: a processor; and the number of the first and second groups,
a memory for storing a computer program;
wherein the processor is configured to perform the incoming call number risk identification method of any of claims 1 to 7 via execution of the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for call number risk identification according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111605148.4A CN114338915A (en) | 2021-12-24 | 2021-12-24 | Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111605148.4A CN114338915A (en) | 2021-12-24 | 2021-12-24 | Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114338915A true CN114338915A (en) | 2022-04-12 |
Family
ID=81013294
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111605148.4A Pending CN114338915A (en) | 2021-12-24 | 2021-12-24 | Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114338915A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116528243A (en) * | 2023-06-29 | 2023-08-01 | 北京华翔联信科技股份有限公司 | User identification method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013050586A2 (en) * | 2011-10-06 | 2013-04-11 | Yougetitback Limited | Systems and methods for monitoring and managing use of mobile electronic devices |
| CN106066884A (en) * | 2016-06-06 | 2016-11-02 | 珠海市小源科技有限公司 | A kind of information security recognition methods and device |
| CN108259680A (en) * | 2016-12-28 | 2018-07-06 | 广东世纪网通信设备股份有限公司 | Fraudulent call recognition methods, device and the server for identifying fraudulent call |
| CN113596844A (en) * | 2021-07-29 | 2021-11-02 | 恒安嘉新（北京）科技股份公司 | Early warning method, device, medium and electronic equipment based on data information |
-
2021
- 2021-12-24 CN CN202111605148.4A patent/CN114338915A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013050586A2 (en) * | 2011-10-06 | 2013-04-11 | Yougetitback Limited | Systems and methods for monitoring and managing use of mobile electronic devices |
| CN106066884A (en) * | 2016-06-06 | 2016-11-02 | 珠海市小源科技有限公司 | A kind of information security recognition methods and device |
| CN108259680A (en) * | 2016-12-28 | 2018-07-06 | 广东世纪网通信设备股份有限公司 | Fraudulent call recognition methods, device and the server for identifying fraudulent call |
| CN113596844A (en) * | 2021-07-29 | 2021-11-02 | 恒安嘉新（北京）科技股份公司 | Early warning method, device, medium and electronic equipment based on data information |
Non-Patent Citations (1)
| Title |
|---|
| 刘大洋, 张阳, 吴金科, 邓兆斌: "基于5G的MEC智慧校园网综合解决方案", 5G网络创新研讨会（2018）论文集, 31 December 2018 (2018-12-31) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116528243A (en) * | 2023-06-29 | 2023-08-01 | 北京华翔联信科技股份有限公司 | User identification method and device, electronic equipment and storage medium |
| CN116528243B (en) * | 2023-06-29 | 2023-09-08 | 北京华翔联信科技股份有限公司 | User identification method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10855840B2 (en) | Systems and methods for automatically conducting risk assessments for telephony communications | |
| CN108924333B (en) | Fraud telephone identification method, device and system | |
| Cecaj et al. | Re-identification of anonymized CDR datasets using social network data | |
| CN110493476B (en) | Detection method, device, server and storage medium | |
| CN112445870B (en) | Knowledge graph string parallel case analysis method based on mobile phone evidence obtaining electronic data | |
| CN111654866A (en) | Method, device and computer storage medium for preventing mobile communication from fraud | |
| CN110489984A (en) | Based reminding method, device, computer equipment and the storage medium of backlog | |
| CN110113748B (en) | Crank call monitoring method and device | |
| Yang et al. | How Twitter data sampling biases US voter behavior characterizations | |
| CN105704337A (en) | Number-marking method and system | |
| CN114338915A (en) | Caller ID risk identification method, caller ID risk identification device, caller ID risk identification equipment and storage medium | |
| CN113923669A (en) | Anti-fraud early warning method, device, equipment and medium for multi-source cross-platform fusion | |
| Green et al. | 'A Spy in your Pocket'? The Regulation of Mobile Data in the UK. | |
| CN110166635B (en) | Suspicious terminal identification method and suspicious terminal identification system | |
| US20220239773A1 (en) | Caller identification information analyzer | |
| Awajan et al. | Machine learning techniques for automated policy violation reporting | |
| KR102294615B1 (en) | e-Business Card Management Service System | |
| CN110830664B (en) | Method and device for identifying telecommunication fraud potential victim user | |
| CN110401959B (en) | Method and device for detecting network rubbing terminal, electronic equipment and storage medium | |
| Rehak | „When Ethics demands the already Present–How Ethics undermines effective data protection in the case of the Corona-Warn-App in Germany “ | |
| Huang et al. | Modeling sharing decision of campus safety reports and its design implications to mobile crowdsourcing for safety | |
| Dreis et al. | Restricted Information Identification Model | |
| Auliya et al. | A review on smartphone usage data for user identification and user profiling | |
| Mansour et al. | Using Mobile Data for Understanding Population Movement and Disease Transmission during Covid-19 Outbreak in the Nordics | |
| Flood et al. | Monitoring the R-Citizen in the Time of Coronavirus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |