CN113286035A - Abnormal call detection method, device, equipment and medium - Google Patents
Abnormal call detection method, device, equipment and medium Download PDFInfo
- Publication number
- CN113286035A CN113286035A CN202110529065.5A CN202110529065A CN113286035A CN 113286035 A CN113286035 A CN 113286035A CN 202110529065 A CN202110529065 A CN 202110529065A CN 113286035 A CN113286035 A CN 113286035A
- Authority
- CN
- China
- Prior art keywords
- call
- data
- abnormal
- call signaling
- voip
- 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.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/66—Substation equipment, e.g. for use by subscribers with means for preventing unauthorised or fraudulent calling
- H04M1/663—Preventing unauthorised calls to a telephone set
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/436—Arrangements for screening incoming calls, i.e. evaluating the characteristics of a call before deciding whether to answer it
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M7/00—Arrangements for interconnection between switching centres
- H04M7/006—Networks other than PSTN/ISDN providing telephone service, e.g. Voice over Internet Protocol (VoIP), including next generation networks with a packet-switched transport layer
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Computer Networks & Wireless Communication (AREA)
- Telephonic Communication Services (AREA)
Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for detecting abnormal calls. The method comprises the following steps: acquiring various types of call record description data; forming at least one multi-source data packet according to the call record description data, wherein the multi-source data packet comprises at least two types of call record description data; and performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results respectively corresponding to each multi-source data group. In the technical scheme, the abnormal call detection result is obtained by performing correlation analysis on the various types of call record description data, so that the abnormal call is effectively and safely monitored, and the detection accuracy of the abnormal call is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication network security, in particular to an abnormal call detection method, device, equipment and medium.
Background
With the continuous forward development of new communication services such as traditional telephone service or VoIP (Voice over Internet Protocol) and the like, the propagation means of various junk calls such as fraud, harassment or promotion and the like are continuously upgraded. For some organizations or individuals, abnormal calls may be initiated for various purposes, which has a great impact on users of the telecommunication network who normally use the telecommunication service.
At present, most abnormal calls are initiated abroad and fall on the ground at home, the calling position is difficult to determine, and most of the calling numbers of the abnormal calls are false calling parties, so that the calling numbers of the abnormal calls cannot be accurately judged and early warning can be timely given out, and tracing attack is difficult to carry out. Therefore, how to effectively perform security supervision on the abnormal call and improve the detection accuracy of the abnormal call is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides an abnormal call detection method, an abnormal call detection device, abnormal call detection equipment and an abnormal call detection medium, so that the abnormal call can be effectively and safely monitored, and the detection accuracy of the abnormal call can be improved.
In a first aspect, an embodiment of the present invention provides an abnormal call detection method, including:
acquiring various types of call record description data;
forming at least one multi-source data packet according to the call record description data, wherein the multi-source data packet comprises at least two types of call record description data;
and performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results respectively corresponding to each multi-source data group.
In a second aspect, an embodiment of the present invention further provides an abnormal call detection apparatus, including:
the call record description data acquisition module is used for acquiring various types of call record description data;
the multi-source data grouping generation module is used for forming at least one multi-source data grouping according to the call record description data, and the multi-source data grouping comprises at least two types of call record description data;
and the abnormal call detection result generation module is used for performing correlation analysis on the call record description data included in each multi-source data group to form an abnormal call detection result corresponding to each multi-source data group.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the abnormal call detection method according to any embodiment of the present invention when executing the computer program.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the abnormal call detection method according to any embodiment of the present invention.
In the technical scheme provided by the embodiment of the invention, multiple types of call record description data are obtained and form at least one multi-source data packet comprising at least two types of call record description data, the call record description data in each multi-source data packet is subjected to correlation analysis to form abnormal call detection results respectively corresponding to each multi-source data packet, and the abnormal call detection results are obtained by performing correlation analysis on the multiple types of call record description data, so that the safety supervision on abnormal calls is effectively realized, and the detection accuracy of the abnormal calls is improved.
Drawings
Fig. 1 is a flowchart illustrating an abnormal call detection method according to a first embodiment of the present invention;
fig. 2 is a flowchart illustrating an abnormal call detection method according to a second embodiment of the present invention;
fig. 3 is a flowchart illustrating an abnormal call detection method according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of a logical framework of an abnormal call detection method according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an abnormal call detection apparatus according to a fourth embodiment of the present invention;
fig. 6 is a schematic hardware structure diagram of a computer device in the fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of an abnormal call detection method according to an embodiment of the present invention, where the abnormal call detection method according to an embodiment of the present invention is applicable to a situation where an abnormal call is effectively and safely monitored, and the abnormal call detection method may be implemented by an abnormal call detection apparatus according to an embodiment of the present invention, and the abnormal call detection apparatus may be implemented in software and/or hardware, and may be generally integrated in a computer device.
As shown in fig. 1, the method for detecting an abnormal call provided in this embodiment specifically includes:
and S110, acquiring various types of call record description data.
The call record description data refers to call data including call detail records, such as a call ticket of a conventional telephone service or a call ticket of a VoIP communication service.
And S120, forming at least one multi-source data group according to the call record description data, wherein the multi-source data group comprises at least two types of call record description data.
The multi-source data grouping refers to a type of data obtained by grouping multiple types of call record description data, and each multi-source data grouping may include at least two types of call record description data.
In view of the fact that the abnormal call detection in the prior art is based on an individual data source, such as internet call data, provincial call data or international call data, and the like, the individual data source is used for detecting abnormal calls of different types, such as long distance calls, cross-country calls and the like, among different operators, but false alarm and false alarm situations generally exist.
S130, performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results corresponding to each multi-source data group.
The abnormal calling detection based on the multi-source data fusion is realized by performing correlation analysis on at least two call record description data, the accuracy of the abnormal calling detection is improved, the abnormal calling behavior early warning can be timely sent out according to the obtained abnormal calling detection results respectively corresponding to the multi-source data groups, and illegal behaviors such as telecom fraud and the like are effectively attacked.
As an alternative implementation, forming at least one multi-source data packet according to the call record description data may include: obtaining VoIP call ticket data and area call ticket data in at least one area to form a first type of multi-source data packet.
Among them, VoIP, that is, Internet phone, refers to a phone communication realized through the Internet or other networks using IP (Internet Protocol) technology; VoIP ticket data refers to a call detail record corresponding to telephone communication generated by a VoIP communication mode; the regional call ticket data refers to call detail records corresponding to telephone communications generated in each region, for example, call ticket data of each provincial region or call ticket data of each city region.
In the embodiment of the invention, because the call behavior in actual life can be a call sent by a mobile communication network of an operator at home, or a VoIP call initiated by connecting an SIP (Session Initiation Protocol) server through a network telephone client at abroad, and because the detection of the international abnormal call in the prior art has certain defects, the detection of the international abnormal call can be realized in a targeted manner by acquiring VoIP call ticket data of an international internet access, and the detection efficiency is improved.
When the first-type multi-source data packet is acquired, S130 may specifically include the following operations S131 to S133:
s131, analyzing each call record in the VoIP call ticket data in the first-class multi-source data grouping by adopting a preset VoIP abnormal call record detection model, and outputting alternative abnormal VoIP call ticket data.
The detection model of the abnormal VoIP call records refers to a machine learning model which is trained in advance and used for detecting suspected abnormal calls in VoIP call ticket data. In the embodiment of the invention, the VoIP abnormal call record detection model can be trained according to the feature data such as number features, suspicious gateways, call behaviors and/or abnormal account numbers.
The alternative abnormal VoIP call ticket data refers to call ticket data obtained through a VoIP abnormal call record detection model, and each call record in the call ticket data is a suspected abnormal call in the detected VoIP call ticket data. Wherein, each call record in the alternative abnormal VoIP call ticket data at least may include: calling party telephone number, called party attribution information, call starting time, call ending time, call duration and other call record information.
After the first-class multi-source data packet comprising the VoIP ticket data and the regional ticket data in at least one region is obtained, each call record in the VoIP ticket data can be analyzed by adopting a preset VoIP abnormal call record detection model, and the call record corresponding to the abnormal call in the VoIP ticket data can be further identified according to an abnormal call detection model such as a number change model and/or a fraud model obtained by pre-training, so that alternative abnormal VoIP ticket data comprising the call record corresponding to each abnormal call can be obtained.
S132, according to the called attribution information of each call record in the alternative abnormal VoIP phone bill data, performing area division on the alternative abnormal VoIP phone bill data to obtain alternative abnormal VoIP phone bill data corresponding to each area.
After the alternative abnormal VoIP phone bill data is obtained, the alternative abnormal VoIP phone bill data is divided into alternative abnormal VoIP phone bill data corresponding to each area according to called attribution information of each call record contained in the alternative abnormal VoIP phone bill data.
S133, according to the respective corresponding call records in the alternative abnormal VoIP phone bill data and the area phone bill data, performing association matching on the alternative abnormal VoIP phone bill data and the area phone bill data corresponding to the alternative abnormal VoIP phone bill data to obtain abnormal VoIP phone bill data serving as an abnormal call detection result.
According to the call records in the alternative abnormal VoIP call ticket data and the call records in the regional call ticket data in at least one region, based on the call record information such as the call time and/or the call number, the consistency association matching analysis can be carried out on the alternative abnormal VoIP call ticket data and the regional call ticket data corresponding to the alternative abnormal VoIP call ticket data, the call records in the alternative abnormal VoIP call ticket data which are successfully matched are used as the abnormal VoIP call records, and then the abnormal VoIP call ticket data containing the abnormal VoIP call records are obtained, and the abnormal call detection result is obtained. By associating the VoIP call ticket data with the regional call ticket data, the abnormal call detection of the communication user based on the multi-source data is realized, and the detection accuracy of the abnormal call is improved.
Exemplarily, after the alternative abnormal VoIP phone bill data is subjected to region division, the alternative abnormal VoIP phone bill data corresponding to the province A and the alternative abnormal VoIP phone bill data corresponding to the province B can be obtained, according to the alternative abnormal VoIP phone bill data, the area phone bill data of province A and the area phone bill data of province B, corresponding call records are respectively carried out, the alternative abnormal VoIP phone bill data corresponding to the province A and the area phone bill data of the province A are subjected to correlation matching to obtain the abnormal VoIP phone bill data of the province A, the alternative abnormal VoIP phone bill data corresponding to the province B and the area phone bill data of the province B are subjected to correlation matching to obtain the abnormal VoIP phone bill data of the province B, and the obtained abnormal VoIP phone bill data corresponding to the province A and the abnormal VoIP phone bill data of the province B are used as abnormal call detection results.
Further, after the abnormal VoIP ticket data is obtained, the abnormal call marking may be performed on each call record in the abnormal VoIP ticket data, for example, the calling party telephone number of each call record in the abnormal VoIP ticket data may be marked as an abnormal calling party number (such as a fraud number), and the called party telephone number is marked as a victim number. In addition, the abnormal call marking result may be used as an abnormal call detection result, and an abnormal call behavior early warning may be sent.
It should be noted that, in the embodiment of the present invention, the following operations may also be adopted to detect an abnormal call, specifically: dividing the VoIP call ticket data into areas to obtain VoIP call ticket data corresponding to each area; according to the respective corresponding call records in the VoIP call ticket data and the regional call ticket data, performing association matching on the VoIP call ticket data and the regional call ticket data corresponding to the VoIP call ticket data to obtain alternative call ticket data, wherein the alternative call ticket data comprises at least one call record which is successfully associated and matched; and according to the calling party telephone number of each call record in the alternative call ticket data, carrying out communication regularity analysis on each call record to obtain an abnormal call telephone number as an abnormal call detection result. That is to say, the VoIP phone bill data may be divided into regions, and then associated and matched with the corresponding region phone bill data, and then the obtained matching result is analyzed for communication regularity by using a machine learning algorithm or a pre-trained learning model, and finally the obtained abnormal call phone number is used as an abnormal call detection result.
According to the technical scheme provided by the embodiment of the invention, the multiple types of call record description data are obtained and form at least one multi-source data packet comprising at least two types of call record description data, the call record description data in each multi-source data packet are subjected to correlation analysis to form abnormal call detection results respectively corresponding to each multi-source data packet, and the abnormal call detection results are obtained by performing correlation analysis on the multiple types of call record description data, so that the safety supervision on the abnormal call is effectively realized, and the detection accuracy of the abnormal call is improved. The technical scheme can be applied to the safety supervision of abnormal calls of types such as fraud, harassment, anti-announcement and the like in the technical field of communication network safety and the supervision of international roaming user tracks.
Example two
Fig. 2 is a flowchart of an abnormal call detection method according to a second embodiment of the present invention. The present embodiment is embodied on the basis of the above embodiments, wherein at least one multi-source data packet may be formed according to each call record description data, specifically: obtaining region call ticket data and call signaling data in at least one region to form a second type multi-source data packet;
performing association analysis on each call record description data included in the multi-source data packet to form abnormal call detection results respectively corresponding to the multi-source data packet, which may include:
acquiring an initiator address and a receiver address respectively corresponding to each call signaling message in call signaling data;
dividing call signaling data according to the address of an initiator and the address of a receiver corresponding to each call signaling message respectively to obtain the outbound call signaling data and the inbound call signaling data;
analyzing each call record in the regional call bill data in the second type of multi-source data grouping by adopting a preset regional abnormal call record detection model, and outputting regional abnormal call bill data;
and respectively carrying out association matching on the outbound call signaling data and the inbound call signaling data and the regional abnormal call ticket data to obtain abnormal outbound call signaling data and abnormal inbound call signaling data which are used as abnormal call detection results.
As shown in fig. 2, the method for detecting an abnormal call provided in this embodiment specifically includes:
and S210, acquiring various types of call record description data.
S220, obtaining area call ticket data and call signaling data in at least one area to form a second type multi-source data packet.
The call signaling data refers to Mobile roaming data generated based on a communication protocol, such as MAP (Mobile Application Part) communication protocol data or Diameter communication protocol data.
S230, acquiring an initiator address and a receiver address corresponding to each call signaling message in the call signaling data.
A call signaling message, which refers to roaming record details in the call signaling data, wherein the call signaling message at least includes: roaming user number, roaming time, originator address, and recipient address.
An originator address (OID), which refers to an IP address corresponding to a mobile communication network entity that initiates an information transfer request when performing a communication service; a receiver address (DID), which refers to an IP address corresponding to a mobile communication network entity that receives and responds to an information delivery request when performing a communication service. For example, it is assumed that the call signaling data is MAP communication protocol data, where the communication services supported by the MAP communication protocol may include location management (i.e., location update and location cancel), authentication management, user data management, call processing, and other types of communication services; when a call processing service is initiated, the IP address corresponding to the party initiating the call request is the address of the initiator, and the IP address corresponding to the party receiving and responding the call request is the address of the receiver.
And acquiring an initiator address and a receiver address corresponding to each call signaling message in the call signaling data.
S240, according to the initiator address and the receiver address respectively corresponding to each call signaling message, dividing the call signaling data to obtain the outbound call signaling data and the inbound call signaling data.
The roaming call signaling data refers to a communication service request initiated in an area outside the service area registered, and is a roaming record roaming into the service area registered, for example, if the service area registered is domestic and the communication service request is a call processing request, the roaming call signaling data is a roaming record corresponding to a call processing request initiated abroad to domestic.
The roaming call signaling data refers to a service request initiated in a service area registered and roams to a roaming record in an area outside the service area registered and registered, for example, if the service area registered and registered is domestic and the communication service request is a call processing request, the roaming call signaling data is a roaming record corresponding to a call service request initiated abroad at home.
According to the initiator address and the receiver address of each call signaling message, the call signaling data can be divided into the outbound call signaling data and the inbound call signaling data.
Optionally, the dividing the call signaling data according to the address of the initiator and the address of the receiver corresponding to each call signaling message may include: and performing association matching on the initiator address and the receiver address respectively corresponding to each call signaling message and the regional work parameter data table, and dividing the call signaling data according to the obtained association matching result.
The regional work parameter data table may be any regional work parameter data table such as a national regional work parameter data table or a regional work parameter data table provided in the prior art, which is not specifically limited in the embodiment of the present invention.
And performing association matching on the address of the initiator and the address of the receiver with the regional work parameter data table, determining respective corresponding regions of the address of the initiator and the address of the receiver, judging whether the regions are in the registered service regions, and further dividing the call signaling data into the outbound call signaling data and the inbound call signaling data.
In the embodiment of the invention, the method can be applied to the communication scene of international roaming by default, namely, the service area of the default registration is domestic, the address of the initiator and the address of the receiver are associated and matched with the national regional work parameter data table, and the call signaling data is divided according to whether the area corresponding to the address of the initiator and the address of the receiver is domestic or foreign.
Further, the associating and matching the address of the initiator and the address of the receiver corresponding to each call signaling message with the regional parameter data table, and dividing the call signaling data according to the obtained associating and matching result may include: dividing the call signaling data according to the operation types respectively corresponding to all call signaling messages in the call signaling data to obtain first class call signaling data and second class call signaling data; and respectively carrying out association matching on the initiator address and the receiver address corresponding to each call signaling message in the first class of call signaling data and the second class of call signaling data with the regional work parameter data table, and respectively dividing the first class of call signaling data and the second class of call signaling data according to the obtained association matching result.
The operation type refers to a communication service type corresponding to each call signaling message in the call signaling data.
It can be understood that, when different communication services are executed, the address of the initiator and the address of the receiver are likely to be different, for example, when the call signaling data is MAP communication protocol data, if the executed communication service is location update, the address of the initiator is a, and the address of the receiver is B; if the executed communication service is location cancellation, the address of the initiator is B, and the address of the receiver is A, that is, location update and location cancellation are opposite execution processes. Therefore, in the embodiment of the present invention, the call signaling data may be divided into two types of call signaling data, the two types of call signaling data are divided according to the association matching result obtained after the corresponding initiator address and receiver address are associated with the regional work parameter data table, and the result obtained by the division is summarized to obtain the outbound call signaling data and the inbound call signaling data.
As a specific implementation manner, when the call signaling data is MAP communication protocol data, the call signaling data may be divided according to an operation code (OP-Type) corresponding to each call signaling message in the MAP communication protocol data, where the operation code refers to an operation code used by the MAP communication protocol when performing a communication service, and a correspondence relationship between the operation code and the communication service is shown in table 1.
Table 1 correspondence of operation codes to communication services
| Operation code | Communication service |
| 1 | Location update |
| 2 | Location cancellation |
| 3 | Shutdown |
| 4 | Calling |
| 5 | Inserting user data |
| 6 | Starting up |
Taking call signaling messages with operation codes of 1, 3, 4 and 6 as first-class call signaling data, taking call signaling messages with operation codes of 2 and 5 as second-class call signaling data, performing association matching on an initiator address and a receiver address corresponding to each call signaling message in the first-class call signaling data and the second-class call signaling data with identification numbers (GT codes) in a national regional project parameter data table, and dividing the first-class call signaling data and the second-class call signaling data according to the obtained association matching result, wherein the method specifically comprises the following steps:
if the address of an initiator corresponding to the call signaling message in the first type of call signaling data is foreign and the address of a receiver is domestic, determining that the call signaling message is a roaming call signaling message, and if the address of the initiator corresponding to the call signaling message in the first type of call signaling data is domestic and the address of the receiver is foreign, determining that the call signaling message is a roaming call signaling message; and if the initiator address corresponding to the call signaling message in the second type of call signaling data is domestic and the receiver address is foreign, determining that the call signaling message is a roaming call signaling message, and if the initiator address corresponding to the call signaling message in the second type of call signaling data is foreign and the receiver address is domestic, determining that the call signaling message is a roaming call signaling message. Then, the outbound call signaling messages in the first type of call signaling data and the second type of call signaling data are summarized, and the outbound call signaling data can be obtained; and summarizing the roaming-in call signaling messages in the first-class call signaling data and the second-class call signaling data to obtain the roaming-in call signaling data.
And S250, analyzing each call record in the regional call bill data in the second type multi-source data grouping by adopting a preset regional abnormal call record detection model, and outputting regional abnormal call bill data.
The detection model of the regional abnormal call record refers to a machine learning model which is trained in advance and used for detecting suspected abnormal calls in regional call list data. In the embodiment of the invention, the detection model of the regional abnormal call record can be trained according to the feature data such as number feature, call behavior and the like.
The regional abnormal call ticket data refers to call ticket data obtained through a regional abnormal call record detection model, and each call record in the call ticket data is a suspected abnormal call in the detected regional call ticket data.
And detecting the call records corresponding to the abnormal calls in the regional call bill data by adopting a preset regional abnormal call record detection model, and further identifying the call records corresponding to the abnormal calls in the regional call bill data according to abnormal call detection models such as a number change model, a harassment model and/or a fraud model obtained by pre-training to obtain the regional abnormal call bill data comprising the call records corresponding to the abnormal calls.
S260, the outbound call signaling data and the inbound call signaling data are respectively associated and matched with the regional abnormal call ticket data to obtain abnormal outbound call signaling data and abnormal inbound call signaling data which serve as abnormal call detection results.
In the embodiment of the invention, according to the roaming time and/or roaming user number and other call signaling message information of each call signaling message in the outbound call signaling data and the inbound call signaling data, consistency association matching analysis is respectively carried out with the regional abnormal call ticket data, the successfully matched call signaling message is used as the abnormal outbound call signaling message and the abnormal inbound call signaling message, and then the abnormal outbound call signaling data and the abnormal inbound call signaling data are obtained, and the abnormal call detection result is obtained.
Further, after the abnormal call detection result is obtained, an abnormal call behavior early warning can be sent, other area early warnings can be sent out and other area early warnings can be sent out according to the outgoing call signaling message and the incoming call signaling message, and corresponding early warning levels can be set.
As an optional implementation manner, after obtaining the abnormal outbound call signaling data and the abnormal inbound call signaling data, the method may further include: counting shutdown users in the abnormal outbound call signaling data and/or the abnormal inbound call signaling data; if the shutdown time of the shutdown user exceeds the preset time threshold, early warning recording is carried out on the shutdown user, and early warning recording of the roaming user is achieved based on the shutdown time and other state behaviors of the roaming user.
The preset duration threshold refers to a preset duration, for example, a week or a month.
In the embodiment of the present invention, for a roaming user, a shutdown user in abnormal outbound call signaling data and/or abnormal inbound call signaling data may be counted according to shutdown time in each call signaling message in the abnormal outbound call signaling data and/or the abnormal inbound call signaling data, and an early warning record may be performed for the outbound user and/or the inbound user who has been shutdown for a long time, for example, a type of a sent early warning, an early warning level, early warning time, and/or early warning times, and the like may be recorded.
For those parts of this embodiment that are not explained in detail, reference is made to the aforementioned embodiments, which are not repeated herein.
According to the technical scheme, the obtained regional call ticket data and the obtained call signaling data in at least one region are used as a type of multi-source data grouping, the call signaling data are divided into the overflow call signaling data and the overflow call signaling data according to the initiator address and the receiver address corresponding to each call signaling message in the call signaling data, and the overflow call signaling data are respectively associated and matched with the regional abnormal call ticket data obtained through analysis, so that the abnormal overflow call signaling data and the abnormal overflow call signaling data can be obtained and used as an abnormal call detection result, the regional call ticket data and the call signaling data are associated, the abnormal call detection of a roaming user based on the multi-source data is realized, and the detection accuracy of the abnormal call is improved.
EXAMPLE III
Fig. 3 is a flowchart of an abnormal call detection method according to a third embodiment of the present invention. The present embodiment is embodied on the basis of the above embodiments, wherein at least one multi-source data packet may be formed according to each call record description data, specifically:
obtaining VoIP call ticket data, area call ticket data in at least one area and a preset abnormal call database to form a third type multi-source data packet;
performing association analysis on each call record description data included in the multi-source data packet to form abnormal call detection results respectively corresponding to the multi-source data packet, which may include:
converting the conversation voice information recorded in the VoIP call ticket data and/or the regional call ticket data into corresponding text information;
and performing correlation matching on the text information and the abnormal call database to obtain abnormal call text information serving as an abnormal call detection result.
As shown in fig. 3, the method for detecting an abnormal call provided in this embodiment specifically includes:
and S310, acquiring various types of call record description data.
S320, obtaining VoIP call ticket data, area call ticket data in at least one area and a preset abnormal call database to form a third type multi-source data packet.
The abnormal call database refers to a preset database for matching call records and determining abnormal calls, such as a fraud phone blacklist.
S330, converting the conversation voice information recorded in the VoIP call ticket data and/or the regional call ticket data into corresponding text information.
In the embodiment of the present invention, any speech recognition method for converting speech into text in the prior art may be adopted to convert the call speech information into corresponding text information, so as to match the text information.
And S340, performing correlation matching on the text information and the abnormal call database to obtain abnormal call text information serving as an abnormal call detection result.
And performing association matching on the text information and the abnormal call database, wherein the successfully matched text information is the abnormal call text information, so that an abnormal call detection result is obtained. For example, the text information is associated and matched with a fraud phone blacklist, and the successfully matched text information can be used as a fraud scenario, which is an abnormal call detection result.
For those parts of this embodiment that are not explained in detail, reference is made to the aforementioned embodiments, which are not repeated herein.
According to the technical scheme, the obtained VoIP call bill data, the regional call bill data in at least one region and the preset abnormal call database are taken as a type of multi-source data grouping, the conversation voice information recorded in the VoIP call bill data and/or the regional call bill data is converted into corresponding text information, then the text information is associated and matched with the abnormal call database to obtain abnormal call text information which is used as an abnormal call detection result, the text information converted from the multi-source data grouping is associated with the abnormal call database, the abnormal call detection based on the text information of the multi-source data is realized, the abnormal call detection mode is further optimized, and the abnormal call detection accuracy is improved.
On the basis of the foregoing embodiments, as a specific implementation manner, fig. 4 provides a schematic diagram of a logic framework of an abnormal call detection method, where a first-type multi-source data packet, a second-type multi-source data packet, and a third-type multi-source data packet may be associated and matched at the same time, and an obtained abnormal call detection result is specifically: firstly, acquiring VoIP call ticket data, regional call ticket data in at least one region, call signaling data and a preset abnormal call database; secondly, performing data analysis on each type of call record description data respectively, obtaining alternative abnormal VoIP call ticket data after analyzing the VoIP call ticket data, obtaining regional abnormal call ticket data after analyzing the regional call ticket data, obtaining outgoing call signaling data and incoming call signaling data after analyzing the call signaling data, wherein a preset abnormal call database can specifically comprise a fraud telephone blacklist, a public security report database and a number knowledge database; then, each multi-source data group is subjected to correlation analysis according to the corresponding correlation dimension; and finally, summarizing abnormal call detection results obtained after the multi-source data are grouped, associated and matched, and sending out early warning.
Example four
Fig. 5 is a schematic structural diagram of an abnormal call detection apparatus according to a fourth embodiment of the present invention, where the embodiment of the present invention is applicable to a situation where an abnormal call is effectively and safely monitored, and the apparatus may be implemented in a software and/or hardware manner, and may be generally integrated in a computer device.
As shown in fig. 5, the abnormal call detection apparatus specifically includes: a call log description data obtaining module 510, a multi-source data packet generating module 520, and an abnormal call detection result generating module 530. Wherein the content of the first and second substances,
a call record description data obtaining module 510, configured to obtain multiple types of call record description data;
a multi-source data grouping generation module 520, configured to form at least one multi-source data grouping according to each call record description data, where the multi-source data grouping includes at least two types of call record description data;
an abnormal call detection result generation module 530, configured to perform correlation analysis on the call record description data included in each multi-source data packet, and form an abnormal call detection result corresponding to each multi-source data packet.
According to the technical scheme provided by the embodiment of the invention, the multiple types of call record description data are obtained and form at least one multi-source data packet comprising at least two types of call record description data, the call record description data in each multi-source data packet are subjected to correlation analysis to form abnormal call detection results respectively corresponding to each multi-source data packet, and the abnormal call detection results are obtained by performing correlation analysis on the multiple types of call record description data, so that the safety supervision on the abnormal call is effectively realized, and the detection accuracy of the abnormal call is improved.
As an optional implementation manner, the multi-source data packet generating module 520 is specifically configured to obtain VoIP ticket data and area ticket data in at least one area, and form a first-type multi-source data packet;
an abnormal call detection result generation module 530, specifically configured to analyze each call record in the VoIP ticket data in the first-class multi-source data packet by using a preset VoIP abnormal call record detection model, and output alternative abnormal VoIP ticket data; according to the called attribution information of each call record in the alternative abnormal VoIP phone bill data, performing area division on the alternative abnormal VoIP phone bill data to obtain alternative abnormal VoIP phone bill data corresponding to each area; and according to the alternative abnormal VoIP call ticket data and the corresponding call records in the regional call ticket data, performing association matching on the alternative abnormal VoIP call ticket data and the regional call ticket data corresponding to the alternative abnormal VoIP call ticket data to obtain abnormal VoIP call ticket data serving as an abnormal call detection result.
As an optional implementation manner, the multi-source data packet generating module 520 is specifically configured to obtain region ticket data and call signaling data in at least one region, and form a second-type multi-source data packet;
the abnormal call detection result generating module 530 further includes: an address acquisition unit, a call signaling data division unit, a regional abnormal ticket data generation unit and an abnormal roaming call signaling data generation unit, wherein,
an address obtaining unit, configured to obtain an address of an initiator and an address of a receiver, where the address of the initiator and the address of the receiver correspond to each call signaling message in the call signaling data, respectively;
a call signaling data dividing unit, configured to divide the call signaling data according to an initiator address and a receiver address respectively corresponding to each call signaling message, so as to obtain a roaming-out call signaling data and a roaming-in call signaling data;
the regional abnormal call ticket data generation unit is used for analyzing each call record in regional call ticket data in the second type multi-source data grouping by adopting a preset regional abnormal call record detection model and outputting regional abnormal call ticket data;
and the abnormal roaming call signaling data generating unit is used for respectively carrying out association matching on the outbound signaling data and the inbound call signaling data and the regional abnormal call ticket data to obtain abnormal outbound signaling data and abnormal inbound call signaling data which are used as abnormal call detection results.
Optionally, the call signaling data dividing unit is specifically configured to perform association matching between an initiator address and a receiver address corresponding to each call signaling message and a regional parameter data table, and divide the call signaling data according to an obtained association matching result to obtain the outbound call signaling data and the inbound call signaling data.
Further, the call signaling data dividing unit is specifically configured to divide the call signaling data according to operation types respectively corresponding to call signaling messages in the call signaling data, so as to obtain first-class call signaling data and second-class call signaling data; and respectively carrying out association matching on an initiator address and a receiver address corresponding to each call signaling message in the first type of call signaling data and the second type of call signaling data with a regional work parameter data table, and respectively dividing the first type of call signaling data and the second type of call signaling data according to the obtained association matching result to obtain the outbound call signaling data and the inbound call signaling data.
Optionally, the apparatus further comprises: the early warning recording module is used for counting shutdown users in the abnormal outbound call signaling data and/or the abnormal inbound call signaling data after the abnormal outbound call signaling data and the abnormal inbound call signaling data are obtained; and if the shutdown time of the shutdown user exceeds a preset time threshold, carrying out early warning record on the shutdown user.
As an optional implementation manner, the multi-source data packet generating module 520 is specifically configured to obtain VoIP ticket data, area ticket data in at least one area, and a preset abnormal call database, and form a third-type multi-source data packet;
an abnormal call detection result generating module 530, configured to convert the call voice information recorded in the VoIP ticket data and/or the regional ticket data into corresponding text information; and performing correlation matching on the text information and the abnormal call database to obtain abnormal call text information serving as an abnormal call detection result.
The abnormal call detection device can execute the abnormal call detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the abnormal call detection method.
EXAMPLE five
Fig. 6 is a schematic diagram of a hardware structure of a computer device according to a fifth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 6 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 6, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement an abnormal call detection method provided by the embodiment of the present invention. That is, the processing unit implements, when executing the program:
acquiring various types of call record description data;
forming at least one multi-source data packet according to the call record description data, wherein the multi-source data packet comprises at least two types of call record description data;
and performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results respectively corresponding to each multi-source data group.
EXAMPLE six
A sixth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an abnormal call detection method according to all embodiments of the present invention: that is, the program when executed by the processor implements:
acquiring various types of call record description data;
forming at least one multi-source data packet according to the call record description data, wherein the multi-source data packet comprises at least two types of call record description data;
and performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results respectively corresponding to each multi-source data group.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An abnormal call detection method, comprising:
acquiring various types of call record description data;
forming at least one multi-source data packet according to the call record description data, wherein the multi-source data packet comprises at least two types of call record description data;
and performing correlation analysis on the call record description data included in each multi-source data group to form abnormal call detection results respectively corresponding to each multi-source data group.
2. The method of claim 1, wherein forming at least one multi-source data packet from each call record description data comprises:
obtaining VoIP phone bill data of a network phone and regional phone bill data in at least one region to form a first type of multi-source data packet;
the method comprises the following steps of performing correlation analysis on call record description data included in multi-source data groups to form abnormal call detection results respectively corresponding to the multi-source data groups, wherein the abnormal call detection results comprise:
analyzing each call record in the VoIP call ticket data in the first-class multi-source data packet by adopting a preset VoIP abnormal call record detection model, and outputting alternative abnormal VoIP call ticket data;
according to the called attribution information of each call record in the alternative abnormal VoIP phone bill data, performing area division on the alternative abnormal VoIP phone bill data to obtain alternative abnormal VoIP phone bill data corresponding to each area;
and according to the alternative abnormal VoIP call ticket data and the corresponding call records in the regional call ticket data, performing association matching on the alternative abnormal VoIP call ticket data and the regional call ticket data corresponding to the alternative abnormal VoIP call ticket data to obtain abnormal VoIP call ticket data serving as an abnormal call detection result.
3. The method of claim 1, wherein forming at least one multi-source data packet from each call record description data comprises:
obtaining region call ticket data and call signaling data in at least one region to form a second type multi-source data packet;
the method comprises the following steps of performing correlation analysis on call record description data included in multi-source data groups to form abnormal call detection results respectively corresponding to the multi-source data groups, wherein the abnormal call detection results comprise:
acquiring an initiator address and a receiver address respectively corresponding to each call signaling message in the call signaling data;
dividing the call signaling data according to the initiator address and the receiver address respectively corresponding to each call signaling message to obtain the outbound call signaling data and the inbound call signaling data;
analyzing each call record in the regional call bill data in the second type of multi-source data grouping by adopting a preset regional abnormal call record detection model, and outputting regional abnormal call bill data;
and respectively carrying out association matching on the outbound call signaling data and the inbound call signaling data and the regional abnormal call ticket data to obtain abnormal outbound call signaling data and abnormal inbound call signaling data which are used as abnormal call detection results.
4. The method of claim 3, wherein dividing the call signaling data according to the address of the initiator and the address of the receiver corresponding to the call signaling messages respectively comprises:
and performing association matching on the initiator address and the receiver address respectively corresponding to each call signaling message and a regional work parameter data table, and dividing the call signaling data according to the obtained association matching result.
5. The method of claim 4, wherein associating and matching the address of the initiator and the address of the receiver corresponding to each of the call signaling messages with a regional parameter data table, and dividing the call signaling data according to the obtained association and matching result comprises:
dividing the call signaling data according to the operation types respectively corresponding to all call signaling messages in the call signaling data to obtain first class call signaling data and second class call signaling data;
and respectively carrying out association matching on an initiator address and a receiver address corresponding to each call signaling message in the first type of call signaling data and the second type of call signaling data with a regional work parameter data table, and respectively dividing the first type of call signaling data and the second type of call signaling data according to the obtained association matching result.
6. The method of claim 3, wherein after obtaining the abnormal outbound call signaling data and the abnormal inbound call signaling data, further comprising:
counting shutdown users in the abnormal outbound call signaling data and/or the abnormal inbound call signaling data;
and if the shutdown time of the shutdown user exceeds a preset time threshold, carrying out early warning record on the shutdown user.
7. The method of claim 1, wherein forming at least one multi-source data packet from each call record description data comprises:
obtaining VoIP call ticket data, area call ticket data in at least one area and a preset abnormal call database to form a third type multi-source data packet;
the method comprises the following steps of performing correlation analysis on call record description data included in multi-source data groups to form abnormal call detection results respectively corresponding to the multi-source data groups, wherein the abnormal call detection results comprise:
converting the conversation voice information recorded in the VoIP call ticket data and/or the regional call ticket data into corresponding text information;
and performing correlation matching on the text information and the abnormal call database to obtain abnormal call text information serving as an abnormal call detection result.
8. An abnormal call detection apparatus, comprising:
the call record description data acquisition module is used for acquiring various types of call record description data;
the multi-source data grouping generation module is used for forming at least one multi-source data grouping according to the call record description data, and the multi-source data grouping comprises at least two types of call record description data;
and the abnormal call detection result generation module is used for performing correlation analysis on the call record description data included in each multi-source data group to form an abnormal call detection result corresponding to each multi-source data group.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1-7 when executing the 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 according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110529065.5A CN113286035B (en) | 2021-05-14 | 2021-05-14 | Abnormal call detection method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110529065.5A CN113286035B (en) | 2021-05-14 | 2021-05-14 | Abnormal call detection method, device, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113286035A true CN113286035A (en) | 2021-08-20 |
| CN113286035B CN113286035B (en) | 2022-12-30 |
Family
ID=77279185
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110529065.5A Active CN113286035B (en) | 2021-05-14 | 2021-05-14 | Abnormal call detection method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113286035B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106686261A (en) * | 2017-01-19 | 2017-05-17 | 腾讯科技（深圳）有限公司 | Information processing method and system |
| CN106791220A (en) * | 2016-11-04 | 2017-05-31 | 国家计算机网络与信息安全管理中心 | Prevent the method and system of telephone fraud |
| CN107506776A (en) * | 2017-01-16 | 2017-12-22 | 恒安嘉新（北京）科技股份公司 | A kind of analysis method of fraudulent call number |
| CN110401779A (en) * | 2018-04-24 | 2019-11-01 | 中国移动通信集团有限公司 | A kind of method, apparatus and computer readable storage medium identifying telephone number |
| CN111432080A (en) * | 2018-12-24 | 2020-07-17 | 北京奇虎科技有限公司 | Ticket data processing method, electronic equipment and computer readable storage medium |
| CN112512052A (en) * | 2021-02-05 | 2021-03-16 | 浙江鹏信信息科技股份有限公司 | Data security anomaly detection method and system based on time segmentation feature statistics |
-
2021
- 2021-05-14 CN CN202110529065.5A patent/CN113286035B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106791220A (en) * | 2016-11-04 | 2017-05-31 | 国家计算机网络与信息安全管理中心 | Prevent the method and system of telephone fraud |
| CN107506776A (en) * | 2017-01-16 | 2017-12-22 | 恒安嘉新（北京）科技股份公司 | A kind of analysis method of fraudulent call number |
| CN106686261A (en) * | 2017-01-19 | 2017-05-17 | 腾讯科技（深圳）有限公司 | Information processing method and system |
| CN110401779A (en) * | 2018-04-24 | 2019-11-01 | 中国移动通信集团有限公司 | A kind of method, apparatus and computer readable storage medium identifying telephone number |
| CN111432080A (en) * | 2018-12-24 | 2020-07-17 | 北京奇虎科技有限公司 | Ticket data processing method, electronic equipment and computer readable storage medium |
| CN112512052A (en) * | 2021-02-05 | 2021-03-16 | 浙江鹏信信息科技股份有限公司 | Data security anomaly detection method and system based on time segmentation feature statistics |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113286035B (en) | 2022-12-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10535019B2 (en) | Bot-based data collection for detecting phone solicitations | |
| CN111885270B (en) | Abnormal communication detection method, device, equipment and storage medium | |
| CN107819747B (en) | Telecommunication fraud association analysis system and method based on communication event sequence | |
| CN108243049B (en) | Telecommunication fraud identification method and device | |
| US11632459B2 (en) | Systems and methods for detecting communication fraud attempts | |
| CN108259680B (en) | Fraud call identification method and device and server for identifying fraud calls | |
| CN112333709B (en) | Cross-network fraud association analysis method and system and computer storage medium | |
| CN112738807A (en) | Method, device, equipment and storage medium for discovering harmful GOIP equipment | |
| CN112995422A (en) | Call control method and device, electronic equipment and storage medium | |
| CN114169438A (en) | Telecommunication network fraud identification method, device, equipment and storage medium | |
| CN117715048A (en) | Telecommunication fraud recognition method, device, electronic equipment and storage medium | |
| JPWO2015194148A1 (en) | Caller identification device, caller identification method, and recording medium for storing caller identification program | |
| US11770475B2 (en) | Computerized system and method for robocall steering | |
| CN113286035B (en) | Abnormal call detection method, device, equipment and medium | |
| CN111813811A (en) | Telecommunication fraud early warning method, device, electronic equipment and medium | |
| CN108259688A (en) | VoIP platforms telephone fraud behavioral value method, apparatus and detecting system | |
| CN108810289B (en) | Internet label canceling method and device | |
| CN114257688A (en) | Telephone fraud identification method and related device | |
| CN102231874A (en) | Short message processing method, device and system | |
| CN114168423A (en) | Abnormal number calling monitoring method, device, equipment and storage medium | |
| CN116418915A (en) | Abnormal number identification method, device, server and storage medium | |
| CN112040068B (en) | False international number identification method, device, equipment and readable storage medium | |
| CN113780407B (en) | Data detection method and device, electronic equipment and storage medium | |
| CN112788016B (en) | Illegal user identification method and device, electronic equipment and storage medium | |
| KR102611390B1 (en) | Phishing Detection System Using Voice Call and Text Trap Server |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |