CN112685610B - False registration account identification method and related device - Google Patents
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Abstract
The application provides a false registration account identification method and a related device, and belongs to the technical field of big data. The method comprises the following steps: acquiring first account data of an account set to be identified and second account data of a white list account set; traversing and combining the device parameter variable fields to obtain a device parameter variable field group; acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data and acquiring a corresponding second field data set from the second account data; the number proportion of the corresponding to-be-identified accounts in the to-be-identified account set of the first field data set is obtained, and the number proportion of the corresponding whitelist accounts in the whitelist account set of the second field data set is obtained; and comparing the number duty ratio of the accounts to be identified with the number duty ratio of the whitelist accounts to obtain false registration accounts. The application also relates to the field of blockchains, and the second account data can be stored in the blockchain. The application effectively improves the coverage rate and the overall identification accuracy of false registration account identification.
Description
Technical Field
The application relates to the technical field of big data, in particular to a false registration account identification method and a related device.
Background
The internet black industry is closely related to the life of people, and the black product usually registers a large number of false account numbers, and the black product has great adverse effect on the normal industry, so that the identification and cleaning of the registered false account numbers are very important.
In identifying and striking false account numbers, existing approaches are wind control rule formation policies, i.e., account numbers hitting wind control rules are considered false account numbers, which are typically summarized based on expert experience, e.g., simulator operations, sdk data parsing anomalies, sandbox virtual environment operations, and so forth. The wind control rule forming strategy has the advantages of quick online, easy explanation and accurate single-point striking, but has the weakness of being easily bypassed by a false account number produced in black, and has lower coverage rate and lower overall recognition accuracy.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the application aims to provide a false registration account identification method and device, which can effectively improve the coverage rate and the overall identification accuracy of false registration account identification.
According to one embodiment of the application, a false registration account number identification method includes: acquiring first account data corresponding to an account set to be identified, and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of a device parameter variable field;
traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;
respectively acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data, and respectively acquiring a second field data set corresponding to the equipment parameter variable field set from the second account data;
Acquiring the number proportion of the to-be-identified accounts corresponding to the first field data set in the to-be-identified account set, and acquiring the number proportion of the whitelist accounts corresponding to the second field data set in the whitelist account set;
Comparing the number duty ratio of the account to be identified corresponding to each equipment parameter variable field group with the number duty ratio of the whitelist account, determining the abnormal equipment parameter variable field group, and determining the account to be identified corresponding to the abnormal equipment parameter variable field group as a false registration account.
In some embodiments of the present application, the performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets includes:
And performing full free quantity traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups.
In some embodiments of the present application, the performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets includes:
Acquiring each equipment parameter variable field with the number of field data smaller than the preset number from the first account data as a field to be aggregated, and taking the rest of each equipment parameter variable field as a first field to be combined;
aggregating the fields to be aggregated to obtain a plurality of second fields to be combined;
And performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of equipment parameter variable field sets.
In some embodiments of the present application, the performing traversal combining on all the first fields to be combined and the second fields to be combined to obtain a plurality of device parameter variable field sets includes:
And randomly acquiring the fields with the preset field number from all the first fields to be combined and the second fields to be combined, and combining to obtain a plurality of equipment parameter variable field groups.
In some embodiments of the present application, the aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:
And according to a preset field correlation standard, the related fields to be aggregated are aggregated to obtain a plurality of second fields to be combined.
In some embodiments of the present application, the aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:
and randomly aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, so that the number of fields of all the first fields to be combined and the second fields to be combined is smaller than or equal to a specific number.
In some embodiments of the present application, the obtaining first account data corresponding to the account set to be identified includes:
Acquiring account data corresponding to a target account set, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field;
acquiring target account numbers of the same field data associated with the environment variable field in the target account number set;
acquiring the number of the target accounts of the same field data associated with the environment variable field;
And determining the abnormal target account numbers based on the account numbers, and acquiring field data of equipment parameter variable fields corresponding to the abnormal target account numbers as first account data corresponding to the account sets to be identified.
In some embodiments of the present application, the determining the abnormal target account number based on the account number includes:
And when the number of the accounts is greater than or equal to the number of the preset accounts, determining the abnormal target account by the target account of the same field data associated with the environment variable field.
According to another embodiment of the present application, a false registration account number identification apparatus includes:
The first acquisition module is used for acquiring first account data corresponding to an account set to be identified and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of equipment parameter variable fields;
The combination module is used for performing traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;
The second acquisition module is used for respectively acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data and a second field data set corresponding to the equipment parameter variable field set from the second account data;
The third acquisition module is used for acquiring the number proportion of the accounts to be identified, corresponding to the first field data set in the account set to be identified, and acquiring the number proportion of the whitelist accounts, corresponding to the second field data set in the whitelist account set;
The determining module is used for comparing the number proportion of the accounts to be identified corresponding to each equipment parameter variable field group with the number proportion of the whitelist accounts, determining the abnormal equipment parameter variable field group, and determining the accounts to be identified corresponding to the abnormal equipment parameter variable field group as false registration accounts.
According to another embodiment of the present application, an electronic device may include: a memory storing computer readable instructions; a processor reads the computer readable instructions stored by the memory to perform the method as described above.
According to another embodiment of the application, a computer program medium has stored thereon computer readable instructions, which when executed by a processor of a computer, cause the computer to perform the method as described above.
According to the embodiment of the application, first account data corresponding to an account set to be identified is obtained, second account data corresponding to a white list account set is obtained, and field data of equipment parameter variable fields are included in the first account data and the second account data; then, traversing and combining the device parameter variable fields to obtain a plurality of device parameter variable field sets; respectively acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data, and respectively acquiring a second field data set corresponding to the equipment parameter variable field set from the second account data; then, the number proportion of the to-be-identified accounts corresponding to the first field data set in the to-be-identified account set is obtained, and the number proportion of the white list accounts corresponding to the second field data set in the white list account set is obtained; and finally, comparing the number proportion of the accounts to be identified corresponding to each equipment parameter variable field group with the number proportion of the whitelist accounts, determining an abnormal equipment parameter variable field group, and determining the accounts to be identified corresponding to the abnormal equipment parameter variable field group as false registration accounts.
Furthermore, traversing and combining are carried out through the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups, so that a large number of aggregation conditions of account numbers caused by popular equipment parameter variable fields are avoided, the collected equipment parameters can be subjected to full statistics, the defect that identification can only be carried out by using a small number of parameters in the related technology is avoided, more full utilization of data information materials which can be used by wind control can be realized, and the identification coverage rate is effectively improved; the device parameter field group is used for improving the guide accuracy of device parameters to false account aggregation, has strong robustness to black product variation and is not easy to bypass by black products; and the overall identification accuracy of the false account number is effectively improved.
Other features and advantages of the application will be apparent from the following detailed description taken in conjunction with the accompanying drawings, or may be learned by the practice of the application in part.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
FIG. 1 shows a schematic diagram of a system to which embodiments of the application may be applied.
Fig. 2 shows a flow chart of a false registration account number identification method according to one embodiment of the application.
Fig. 3 shows a flow chart of a false registration account number identification method according to yet another embodiment of the present application.
Fig. 4 shows a block diagram of a false registration account number identification device according to one embodiment of the present application.
Fig. 5 shows a block diagram of an electronic device according to an embodiment of the application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 shows a schematic diagram of a system 100 in which embodiments of the application may be applied.
As shown in fig. 1, the system 100 may include a server 101 and a terminal 102. The terminal 102 and the server 101 may be directly or indirectly connected through wireless communication, and the present application is not particularly limited herein. The server 101 may collect the device parameters and the environment variable parameters of the registered account number on the terminal 102. The server 101 may be a cloud server. The server 101 may also be configured to identify a false registration account number by using a node in the blockchain network, and the server 101 may share the identification result with the blockchain network in which the false registration account number is located, and obtain relevant data of the shared whitelist account number on the blockchain.
In one embodiment of the present example, as shown in fig. 1, the server 101 obtains first account data corresponding to an account set to be identified, and obtains second account data corresponding to a whitelist account set, where the first account data and the second account data both include field data of a device parameter variable field; then, traversing and combining the device parameter variable fields to obtain a plurality of device parameter variable field sets; respectively acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data, and respectively acquiring a second field data set corresponding to the equipment parameter variable field set from the second account data; then, the number proportion of the to-be-identified accounts corresponding to the first field data set in the to-be-identified account set is obtained, and the number proportion of the white list accounts corresponding to the second field data set in the white list account set is obtained; and finally, comparing the number duty ratio of the accounts to be identified with the number duty ratio of the whitelist accounts, determining an abnormal first field data set, and determining the accounts to be identified corresponding to the first field data set as false registration accounts.
Fig. 2 schematically illustrates a flow chart of a false registration account number identification method according to one embodiment of the application. The subject of execution of the false registration account number identification method may be an electronic device having a calculation processing function, such as the server 101 or the terminal 102 shown in fig. 1.
As shown in fig. 2, the false registration account number identification method may include steps S210 to S250.
Step S210, acquiring first account data corresponding to an account set to be identified, and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of a device parameter variable field;
Step S220, performing traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field sets;
Step S230, respectively obtaining a first field data set corresponding to each device parameter variable field set from the first account data, and respectively obtaining a second field data set corresponding to each device parameter variable field set from the second account data;
Step S240, obtaining the number ratio of the to-be-identified accounts corresponding to each first field data set in the to-be-identified account set, and obtaining the number ratio of the whitelist accounts corresponding to each second field data set in the whitelist account set;
Step S250, comparing the number ratio of the accounts to be identified corresponding to each device parameter variable field set with the number ratio of the whitelist accounts, determining the device parameter variable field set with abnormality, and determining the account to be identified corresponding to the device parameter variable field set with abnormality as a false registration account.
Furthermore, traversing and combining are carried out through the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups, so that a large number of aggregation conditions of account numbers caused by popular equipment parameter variable fields are avoided, the collected equipment parameters can be subjected to full statistics, the defect that identification can only be carried out by using a small number of parameters in the related technology is avoided, more full utilization of data information materials which can be used by wind control can be realized, and the identification coverage rate is effectively improved; the device parameter field group is used for improving the guide accuracy of device parameters to false account aggregation, has strong robustness to black product variation and is not easy to bypass by black products; and the overall identification accuracy of the false account number is effectively improved.
The specific procedure of each step performed in false registration account number identification is described below.
In step S210, first account data corresponding to an account set to be identified is obtained, and second account data corresponding to a whitelist account set is obtained, where the first account data and the second account data both include field data of a device parameter variable field.
In this example embodiment, the account set to be identified is used to identify the included false account number, which may be a collection of account numbers collected by a certain enterprise or platform. The first account data corresponding to the account set to be identified is account related attribute data of the account to be identified.
The white list account number set is a collection of collected normal accounts, and can be account related attribute data collected by a tool kit when registering accounts of staff members, agents, purchase insurance policy users and the like in a certain company.
The device parameter variable field is a relevant parameter of the device for which the account number is registered, for example, an installation system, a device model, the number of applications, and the like. The account number as opposed to the device parameter variable field also typically includes field data for an environment variable field, such as a device address, wireless network address, location information, and the like.
In one embodiment, referring to fig. 3, obtaining first account data corresponding to an account set to be identified includes:
Step S310, acquiring account data corresponding to a target account set, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field;
Step S320, obtaining a target account number of the same field data associated with the environment variable field in the target account number set;
step S330, obtaining the account number of the target account number of the same field data associated with the environment variable field;
step S340, determining the abnormal target account number based on the account number, and obtaining field data of a device parameter variable field corresponding to the abnormal target account number as first account data corresponding to the account set to be identified.
In this example embodiment, the set of accounts to be identified is a set of fields to be identified that have false account suspicions that were verified in advance by the environment variable fields. Environment variable field device address, wireless network address, location information, etc.
The set of target accounts may be all accounts that a certain platform collects for this verification task. The method includes the steps of obtaining target accounts of the same field data associated with the environment variable field in a target account set, for example, obtaining target accounts of all the target accounts associated with wireless network address fields (wireless network address field data) in the target account set, and then when the number of the target accounts is more (for example, exceeds the number of preset accounts), indicating that the target accounts have obvious aggregation to a certain extent, and further have false registration account suspicion. Furthermore, an abnormal target account number can be determined based on the account number, and then the abnormal target account number is used as an account set to be identified.
In one embodiment, determining the abnormal target account number based on the account number includes:
and when the number of the accounts is greater than or equal to the number of the preset accounts, determining the target account of the same field data associated with the environment variable field as the abnormal target account.
The number of the preset account numbers can be set according to actual conditions. When the number of the target accounts of the same field data associated with the environment variable field is greater than or equal to the number of the preset accounts, the account of the same field data associated with the environment variable field is shown to have aggregation and suspicion.
In step S220, the device parameter variable fields are subjected to traversal combination, so as to obtain a plurality of device parameter variable field sets.
In this exemplary embodiment, the fields are combined by traversing, which is to combine several fields together to form a field group. Because the device parameter variable fields generally include popular fields, such as a system and a device model, field data corresponding to the popular fields are generally associated with a large number of accounts, for example, an android system is associated with a large number of accounts; the device parameter variables also include personalized fields such as the number of applications, which are typically installed on different devices, such that fewer account numbers are associated with the number of applications. When registering an account, if the account is a normal account, data corresponding to the personalized device parameter variable fields among the accounts are not similar in a large amount, and data corresponding to the popular device parameter variable fields may be similar in a large amount.
By performing traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups, the subsequent analysis can effectively avoid a large number of aggregation of accounts caused by the popular equipment parameter variable fields, and the account is misjudged to be the false account with batch aggregation of black registration.
In one embodiment, performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets, including:
And performing full free quantity traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups.
The number of the device parameter variable fields is usually huge, for example, the number of the device parameter variable fields is usually more than 100, the full free-amount traversal combination is to freely combine the fields corresponding to each account, and the calculated amount of the traversal combination is calculated at the momentWhen the number of the device parameter variable fields exceeds 100, the calculated amount of the full free amount traversal combination is/>Where n is a field. The total free amount traversal combination is large in calculation amount, but can be applied to various field combination cases.
In one embodiment, performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets, including:
Acquiring each equipment parameter variable field with the number of field data smaller than the preset number from the first account data as a field to be aggregated, and taking the rest of each equipment parameter variable field as a first field to be combined;
aggregating the fields to be aggregated to obtain a plurality of second fields to be combined;
And performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of equipment parameter variable field sets.
The number of field data included in the device parameter variable field is smaller than the predetermined number, that is, the number of field data corresponding to a certain device parameter variable field is smaller than the predetermined number, for example, the field data of the field of the device system is only 2, android and ios. The field data of this field of the device model may be hundreds, such as hua, millet, red rice, vivo, apple, etc. Further, when the predetermined number is 5, the device system field may be acquired as a field to be aggregated, and the remaining, e.g., device model field may be taken as a first field to be combined.
And aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, namely combining the plurality of fields to be aggregated together as one field. The aggregation of the fields to be aggregated may be to randomly acquire a predetermined number of fields to be aggregated, or may be to aggregate some fields to be aggregated having correlation.
Therefore, when all the first fields to be combined and the second fields to be combined are subjected to traversal combination to obtain a plurality of equipment parameter variable field groups, the total number of fields is reduced due to advanced combination, and the calculation amount of traversal combination is effectively reduced.
In one embodiment, performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of device parameter variable field sets, including:
And randomly acquiring the fields with the preset field number from all the first fields to be combined and the second fields to be combined, and combining to obtain a plurality of equipment parameter variable field groups.
The predetermined number of fields may be set according to the accuracy requirement, for example, the predetermined number of fields is 3; by acquiring the fields of the predetermined number of fields for combination, the combination of the fields to be aggregated is performed in advance in the foregoing embodiment, and the inventor finds that only the fields of the predetermined number of fields are required to be acquired for combination, and then can accurately judge the abnormal field combination in the subsequent step to determine the false account number.
Meanwhile, the method for avoiding the full free quantity traversal combination comprises more redundant calculation quantity when calculating the quantity of the flow under the condition of full quantity equipment parameter combination.
In one embodiment, the predetermined number of fields is 1 or more and 3 or less.
The full free quantity traversal combination is to calculate the quantity of flow under the condition of the full quantity equipment parameter combination. In practice, too much redundancy calculation is included, and the applicant finds that the number of flows can be effectively met by combining some fields together in advance to be used as one field and then calculating the number of flows under the condition of combining 1-3 device parameter fields. And the calculated amount is calculated byReduced to/>
In one embodiment, aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:
And according to a preset field correlation standard, the related fields to be aggregated are aggregated to obtain a plurality of second fields to be combined.
The predetermined field correlation standard can be stored through a field correlation association table, that is, the association relationship between fields with correlation is stored in the field correlation association table, and further, fields with correlation in fields to be aggregated can be searched for and aggregated, so that the degree of distinction of coarse-grained fields (that is, the number of field data corresponding to the fields is smaller) can be effectively enhanced through the second fields to be combined (that is, the number of accounts corresponding to the second fields to be combined is obviously reduced compared with the number of accounts corresponding to the single fields to be aggregated).
In one embodiment, aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:
and randomly aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, so that the number of fields of all the first fields to be combined and the second fields to be combined is smaller than or equal to a specific number.
The number of fields of the second fields to be combined, which is obtained by controlling the fields to be combined to randomly aggregate, is controlled so that the number of fields of all the first fields to be combined and the second fields to be combined is smaller than or equal to a specific number, the effect of advanced aggregation can be ensured, and the total number of fields in the subsequent traversal combination is ensured to be smaller than or equal to the specific number.
In step S230, a first field data set corresponding to the device parameter variable field set is obtained from each of the first account data, and a second field data set corresponding to the device parameter variable field set is obtained from each of the second account data.
In this embodiment of the present disclosure, the first account data corresponds to an account set to be identified, and the first field data set corresponding to each device parameter variable field set is obtained from the first account data, so that the first field data set corresponding to each device parameter variable field set of each account to be identified may be obtained.
The second account data corresponds to the white list account set, the second field data set corresponding to each equipment parameter variable field set is obtained from the second account data, and the second field data set corresponding to each equipment parameter variable field set of each white list account can be obtained.
In step S240, the number ratio of the accounts to be identified corresponding to each first field data set in the account set to be identified is obtained, and the number ratio of the whitelist accounts corresponding to each second field data set in the whitelist account set is obtained.
In the embodiment of the present example, the number ratio of the to-be-identified accounts corresponding to each first field data set in the to-be-identified account set is obtained, that is, the number ratio of the to-be-identified accounts corresponding to each first field data set in all accounts in the to-be-identified account set is obtained; and similarly, acquiring the number proportion of the corresponding white list accounts in the white list account set of each second field data set, namely acquiring the number proportion of the corresponding white list accounts in all accounts in the white list account set of each second field data set.
The comparison content of the number ratio of the accounts to be identified and the number ratio of the whitelist accounts in one example is shown as follows.
In step S250, the number ratio of the accounts to be identified corresponding to each device parameter variable field set is compared with the number ratio of the whitelist accounts, the abnormal device parameter variable field set is determined, and the accounts to be identified corresponding to the abnormal device parameter variable field set are determined to be false registration accounts.
In this embodiment of the present example, the number ratio of the to-be-identified accounts corresponding to each device parameter variable field set and the number ratio of the whitelist accounts are compared, that is, the number ratio of the to-be-identified accounts corresponding to the first field data set and the number ratio of the whitelist accounts corresponding to the second field data set corresponding to the same device parameter variable field set are obtained, and then the two are compared. In this way, when the number of accounts to be identified is more differentiated from the number of whitelist accounts (the difference between the number of accounts to be identified and the number of whitelist accounts is greater than or equal to the preset duty ratio, or the ratio of the number of accounts to be identified to the number of whitelist accounts is greater than or equal to the preset duty ratio, or the first medium comparison condition (the number of accounts to be identified is "very large" and the number of whitelist accounts is "very small") is satisfied as shown in the above table), it is indicated that there is an abnormality in the first field data set corresponding to the same equipment parameter variable field set, which is different from the normal condition, and then the account to be identified corresponding to the first field data set corresponding to the equipment parameter variable field set can be determined as a false registration account.
The application can carry out overall statistics on the collected equipment parameters, can realize more full utilization on the data information materials which can be used by wind control, and effectively improves the identification coverage rate; the method has stronger robustness to black product variation and is not easy to be bypassed by black products; and the false account number identification accuracy is effectively improved.
Fig. 4 shows a block diagram of a false registration account number identification device according to one embodiment of the present application.
As shown in fig. 4, the false registration account number identification apparatus 400 may include a first acquisition module 410, a combination module 420, a second acquisition module 430, a third acquisition module 440, and a determination module 450.
The first obtaining module 410 may be configured to obtain first account data corresponding to an account set to be identified, and obtain second account data corresponding to a whitelist account set, where the first account data and the second account data both include field data of a device parameter variable field;
the combination module 420 is configured to perform traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets;
The second obtaining module 430 is configured to obtain, from the first account data, a first field data set corresponding to the device parameter variable field set, and obtain, from the second account data, a second field data set corresponding to the device parameter variable field set, respectively;
The third obtaining module 440 is configured to obtain a number duty ratio of the to-be-identified accounts corresponding to the first field data set in the to-be-identified account set, and obtain a number duty ratio of the whitelist accounts corresponding to the second field data set in the whitelist account set;
The determining module 450 is configured to compare the number duty ratio of the to-be-identified accounts corresponding to each device parameter variable field set with the number duty ratio of the whitelist accounts, determine the device parameter variable field set that is abnormal, and determine the to-be-identified account corresponding to the device parameter variable field set that is abnormal as a false registration account.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Fig. 5 schematically shows a block diagram of an electronic device according to an embodiment of the application.
It should be noted that, the electronic device 500 shown in fig. 5 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 5, the electronic apparatus 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the system operation are also stored. The CPU 501, ROM 502, and RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (local area network) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present application, the processes described below with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511. When executed by a Central Processing Unit (CPU) 501, performs the various functions defined in the system of the present application.
The computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: 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. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, wire, fiber optic cable, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application 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 application pertains.
It will be understood that the application is not limited to the embodiments which have been described above and shown in the drawings, but that various modifications and changes can be made without departing from the scope thereof.
Claims (6)
1. A false registration account number identification method, comprising:
Acquiring first account data corresponding to an account set to be identified, and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of a device parameter variable field;
traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;
respectively acquiring a first field data set corresponding to each equipment parameter variable field set from the first account data, and respectively acquiring a second field data set corresponding to each equipment parameter variable field set from the second account data;
acquiring the number proportion of the to-be-identified accounts corresponding to each first field data set in the to-be-identified account set, and acquiring the number proportion of the whitelist accounts corresponding to each second field data set in the whitelist account set;
Comparing the number duty ratio of the account to be identified corresponding to each equipment parameter variable field group with the number duty ratio of the whitelist account, determining the abnormal equipment parameter variable field group, and determining the account to be identified corresponding to the abnormal equipment parameter variable field group as a false registration account;
The traversing combination is performed on the device parameter variable fields to obtain a plurality of device parameter variable field sets, including: acquiring each equipment parameter variable field with the number of field data smaller than the preset number from the first account data as a field to be aggregated, and taking the rest of each equipment parameter variable field as a first field to be combined; aggregating the fields to be aggregated to obtain a plurality of second fields to be combined; performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of equipment parameter variable field groups;
and performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of device parameter variable field sets, including: randomly acquiring a predetermined number of fields from all the first fields to be combined and the second fields to be combined, and combining to obtain a plurality of equipment parameter variable field groups;
The obtaining the first account data corresponding to the account set to be identified includes: acquiring account data corresponding to a target account set, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field; acquiring target account numbers of the same field data associated with the environment variable field in the target account number set; acquiring the number of the target accounts of the same field data associated with the environment variable field; determining the abnormal target account numbers based on the account numbers, and acquiring field data of equipment parameter variable fields corresponding to the abnormal target account numbers as first account data corresponding to the account sets to be identified;
The determining the abnormal target account number based on the account number includes: and when the number of the accounts is greater than or equal to the number of the preset accounts, determining the target account of the same field data associated with the environment variable field as the abnormal target account.
2. The method of claim 1, wherein said traversing the device parameter variable fields to obtain a plurality of device parameter variable field sets comprises:
And performing full free quantity traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups.
3. The method of claim 1, wherein aggregating the fields to be aggregated into a plurality of second fields to be combined comprises:
And according to a preset field correlation standard, the related fields to be aggregated are aggregated to obtain a plurality of second fields to be combined.
4. The method of claim 1, wherein aggregating the fields to be aggregated into a plurality of second fields to be combined comprises:
and randomly aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, so that the number of fields of all the first fields to be combined and the second fields to be combined is smaller than or equal to a specific number.
5. A false registration account number identification device, comprising:
The first acquisition module is used for acquiring first account data corresponding to an account set to be identified and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of equipment parameter variable fields;
The combination module is used for performing traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;
The second acquisition module is used for respectively acquiring a first field data set corresponding to the equipment parameter variable field set from the first account data and a second field data set corresponding to the equipment parameter variable field set from the second account data;
The third acquisition module is used for acquiring the number proportion of the to-be-identified accounts corresponding to the first field data set in the to-be-identified account set and acquiring the number proportion of the white list accounts corresponding to the second field data set in the white list account set;
the determining module is used for comparing the number proportion of the accounts to be identified corresponding to each equipment parameter variable field group with the number proportion of the whitelist accounts, determining the abnormal equipment parameter variable field group and determining the accounts to be identified corresponding to the abnormal equipment parameter variable field group as false registration accounts;
The traversing combination is performed on the device parameter variable fields to obtain a plurality of device parameter variable field sets, including: acquiring each equipment parameter variable field with the number of field data smaller than the preset number from the first account data as a field to be aggregated, and taking the rest of each equipment parameter variable field as a first field to be combined; aggregating the fields to be aggregated to obtain a plurality of second fields to be combined; performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of equipment parameter variable field groups;
and performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of device parameter variable field sets, including: randomly acquiring a predetermined number of fields from all the first fields to be combined and the second fields to be combined, and combining to obtain a plurality of equipment parameter variable field groups;
The obtaining the first account data corresponding to the account set to be identified includes: acquiring account data corresponding to a target account set, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field; acquiring target account numbers of the same field data associated with the environment variable field in the target account number set; acquiring the number of the target accounts of the same field data associated with the environment variable field; determining the abnormal target account numbers based on the account numbers, and acquiring field data of equipment parameter variable fields corresponding to the abnormal target account numbers as first account data corresponding to the account sets to be identified;
The determining the abnormal target account number based on the account number includes: and when the number of the accounts is greater than or equal to the number of the preset accounts, determining the target account of the same field data associated with the environment variable field as the abnormal target account.
6. An electronic device, comprising: a memory storing computer readable instructions; a processor reading computer readable instructions stored in a memory to perform the method of any one of claims 1-4.
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| CN111339317A (en) * | 2020-02-27 | 2020-06-26 | 平安银行股份有限公司 | User registration identification method and device, computer equipment and storage medium |
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| CN111339317A (en) * | 2020-02-27 | 2020-06-26 | 平安银行股份有限公司 | User registration identification method and device, computer equipment and storage medium |
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