CN114971768B - User account identification method and device, computer storage medium and electronic equipment - Google Patents

User account identification method and device, computer storage medium and electronic equipment Download PDF

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CN114971768B
CN114971768B CN202210394255.5A CN202210394255A CN114971768B CN 114971768 B CN114971768 B CN 114971768B CN 202210394255 A CN202210394255 A CN 202210394255A CN 114971768 B CN114971768 B CN 114971768B
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access
access information
account
user account
user
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CN114971768A (en
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陆韦霖
刘东鑫
邵壮丰
汪来富
史国水
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China Telecom Corp Ltd
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China Telecom Corp Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract

The disclosure belongs to the technical field of business security wind control, and relates to a user account identification method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring all access information, and counting the access times corresponding to all access information; according to the access times, corresponding weight values are distributed for the access information; based on the weight value and the access information, creating an access vector corresponding to the user account, and calculating the access vector to obtain an account similarity value between the user accounts; and identifying potential wool party accounts in the user accounts according to the account similarity value. In the disclosure, on one hand, potential wool party accounts are identified according to the account similarity value, so that the situation that a user account with abnormal single attribute information or abnormal part of attribute information is identified as a wool party is avoided; on the other hand, a weight value is distributed for the access information according to the access times, so that the duty ratio of the access vector which is accessed frequently later is increased, and the accuracy of the identified potential wool party account is improved.

Description

User account identification method and device, computer storage medium and electronic equipment
Technical Field
The disclosure relates to the technical field of business security wind control, in particular to a user account identification method, a user account identification device, a computer readable storage medium and electronic equipment.
Background
With the increase of internet marketing campaigns, "wool party" accounts, which exclusively predate virtual rewards in marketing campaigns, appear, and these accounts keep track of a large number of resources such as terminal devices, user accounts, and internet protocol addresses.
In the related art, the "wool party" account number can be identified by setting the access frequency threshold of the internet protocol address in unit time, and the "wool party" account number can be identified by collecting the internet protocol address corresponding to the "wool party" account number, however, due to the appearance of the second dialing technology, the internet protocol address can be distributed to a normal user after one usage period is finished, so that the misjudgment rate of the normal user account number is improved.
In view of this, there is a need in the art to develop a new method and apparatus for identifying a user account.
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 disclosure aims to provide a user account identification method, a user account identification device, a computer readable storage medium and electronic equipment, so as to overcome the problem of higher misjudgment rate of a normal user account caused by related technology at least to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of an embodiment of the present invention, there is provided a user account identification method, the method including: acquiring all access information, and counting the access times corresponding to all the access information; distributing corresponding weight values for the access information according to the access times; based on the weight value and the access information, creating an access vector corresponding to the user account, and calculating the access vector to obtain an account similarity value between the user accounts; and identifying potential wool party accounts in the user accounts according to the account similarity value.
In an exemplary embodiment of the invention, the access information includes an internet protocol address and a uniform resource locator corresponding to a page accessed by the user.
In an exemplary embodiment of the present invention, the assigning a corresponding weight value to the access information according to the access number includes: if the access times are greater than a times threshold, sorting the access information according to the access times to obtain an access sorting result; and distributing corresponding weight values for the access information according to the access sequencing result.
In an exemplary embodiment of the present invention, the allocating a corresponding weight value to the access information according to the access ordering result includes: dividing the access information according to the access ordering result to obtain a dividing result, and determining access times ordering relation among different dividing results; and according to the access times ordering relation, different weight values are allocated to the access information belonging to different division results.
In an exemplary embodiment of the invention, the access information comprises the internet protocol address; the creating an access vector corresponding to the user account based on the weight value and the access information includes: determining a target internet protocol address used by the user account; wherein the target internet protocol address belongs to one or more of the internet protocol addresses; creating a first access vector corresponding to the user account according to the target internet protocol address and the internet protocol address; and determining a weight value corresponding to the target internet protocol address, and creating the weight value and the first access vector.
In an exemplary embodiment of the invention, the access information includes the uniform resource locator; the creating an access vector corresponding to the user account based on the weight value and the access information includes: determining a target uniform resource locator corresponding to the page accessed by the user account; wherein the target uniform resource locator belongs to one or more of the uniform resource locators; creating a second access vector corresponding to the user account according to the target uniform resource locator and the uniform resource locator; and determining a weight value corresponding to the target uniform resource locator, and creating the weight value and the second access vector.
In an exemplary embodiment of the present invention, the calculating the access vector to obtain an account similarity value between the user accounts includes: and calculating the access vector by using a cosine similarity formula to obtain an account similarity value between user accounts.
In an exemplary embodiment of the present invention, the identifying the potential wool party account in the user account according to the account similarity value includes: if the account similarity value is smaller than a first similarity threshold, combining two user accounts corresponding to the account similarity value into one type; and if the number of the user accounts belonging to the class is greater than or equal to a number threshold, identifying the user accounts belonging to the class as potential wool party accounts.
In an exemplary embodiment of the invention, the method further comprises: calculating account similarity values between the user accounts belonging to different classes; and if the account similarity value is smaller than a second similarity threshold value, merging the user accounts belonging to different classes into one class.
According to a second aspect of the embodiment of the present invention, there is provided a user account identification apparatus, the apparatus including: the statistics module is configured to acquire all access information and count the access times corresponding to all the access information; the distribution module is configured to distribute corresponding weight values for the access information according to the access times; the creating device is configured to create an access vector corresponding to the user accounts based on the weight values and the access information so as to calculate the access vector to obtain account similarity values among the user accounts; and the identification device is configured to identify potential wool party accounts in the user accounts according to the account similarity value.
According to a third aspect of an embodiment of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the user account identification method of any of the above-described exemplary embodiments.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the user account identification method in any of the above-described exemplary embodiments.
As can be seen from the above technical solutions, the user account identification method, the user account identification device, the computer storage medium and the electronic device in the exemplary embodiment of the present invention have at least the following advantages and positive effects:
in the method and the device provided by the exemplary embodiment of the disclosure, on one hand, the potential wool party account is identified according to the account similarity value, so that the situation that in the prior art, a user account with abnormal single attribute information or abnormal part of attribute information is identified as the wool party account is avoided; on the other hand, a weight value is distributed for the access information according to the access times, so that the duty ratio of the access vector which is accessed frequently later is increased, and the accuracy of the identified potential wool party account is improved.
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 disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
Fig. 1 schematically illustrates a flowchart of a user account identification method in an embodiment of the disclosure;
fig. 2 schematically illustrates a flowchart of allocating a corresponding weight value to access information in the user account identification method according to an embodiment of the present disclosure;
fig. 3 schematically illustrates a flowchart of allocating a corresponding weight value to access information in the user account identification method according to an embodiment of the present disclosure;
fig. 4 schematically illustrates a flowchart of creating an access vector corresponding to a user account in the user account identification method according to an embodiment of the present disclosure;
fig. 5 schematically illustrates a flowchart of creating an access vector corresponding to a user account in the user account identification method according to an embodiment of the present disclosure;
fig. 6 schematically illustrates a flowchart of identifying a potential wool party account in a user account in the user account identification method according to an embodiment of the present disclosure;
fig. 7 schematically illustrates a flowchart of merging user accounts into one class in the user account identification method in the embodiment of the disclosure;
FIG. 8 schematically illustrates a flow diagram of a method for user account identification in an application scenario;
fig. 9 schematically illustrates a structural diagram of a user account identification apparatus in an embodiment of the present disclosure;
fig. 10 schematically illustrates an electronic device for a user account identification method in an embodiment of the disclosure;
Fig. 11 schematically illustrates a computer-readable storage medium for a user account identification method in an embodiment of the present disclosure.
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. 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 present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.; the terms "first" and "second" and the like are used merely as labels, and are not intended to limit the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
Aiming at the problems in the related art, the present disclosure proposes a user account identification method. Fig. 1 shows a flowchart of a user account identification method, and as shown in fig. 1, the user account identification method at least includes the following steps:
s110, acquiring all access information, and counting the access times corresponding to all access information.
And S120, distributing corresponding weight values for the access information according to the access times.
S130, based on the weight values and the access information, creating an access vector corresponding to the user accounts, and calculating the access vector to obtain account similarity values among the user accounts.
And S140, identifying potential wool party accounts in the user accounts according to the account similarity value.
In the method and the device provided by the exemplary embodiment of the disclosure, on one hand, the potential wool party account is identified according to the account similarity value, so that the situation that in the prior art, a user account with abnormal single attribute information or abnormal part of attribute information is identified as the wool party account is avoided; on the other hand, a weight value is distributed for the access information according to the access times, so that the duty ratio of the access vector which is accessed frequently later is increased, and the accuracy of the identified potential wool party account is improved.
In step S110, all access information is acquired, and the number of accesses corresponding to all access information is counted.
In the exemplary embodiment of the present disclosure, the access information refers to information that is required when a user accesses a certain page by using a user account, specifically, the access information may be an internet protocol (intellectual property, IP) address, the access information may also be a uniform resource locator (Uniform Resource Locator, URL) corresponding to the page accessed by the user, and the access information may also be information that is required when any user accesses a certain page by using the user account, which is not limited in this exemplary embodiment.
In this case, the number of accesses may be the number of times a certain IP address is used, or the number of times a page corresponding to a certain URL is accessed.
For example, there are 4 IP addresses, IP1, IP2, IP3, and IP4, respectively, and there are 3 URLs, URL1, URL2, and URL3, respectively, the number of times a page is accessed using IP1 is 5, the number of times a page is accessed using IP2 is 2, the number of times a page is accessed using IP3 is 4, the number of times a page is accessed using IP4 is 1, the number of times a page corresponding to URL1 is accessed is 3, the number of times a page corresponding to URL2 is accessed is 6, and the number of times a page corresponding to URL3 is accessed is 3.
Based on this, the number of accesses corresponding to IP1 is 5, the number of accesses corresponding to IP2 is 2, the number of accesses corresponding to IP3 is 4, the number of accesses corresponding to IP4 is 1, the number of accesses corresponding to URL1 is 3, the number of accesses corresponding to URL2 is 6, and the number of accesses corresponding to URL3 is 3.
In an alternative embodiment, the access information includes an internet protocol address and a uniform resource locator corresponding to a page accessed by the user account.
The access information specifically includes an internet protocol (intellectual property, IP) address and a uniform resource locator (Uniform Resource Locator, URL).
For example, in a certain virtual marketing campaign, the access information includes URLs corresponding to all pages that can be accessed, and the access information further includes IP addresses that can be used by the user account.
In the present exemplary embodiment, the access information includes an internet protocol address and a uniform resource locator, which lays a foundation for subsequent creation of an access vector from the internet protocol address and a vector from the uniform resource locator.
In step S120, corresponding weight values are assigned to the access information according to the number of accesses.
In an exemplary embodiment of the present disclosure, corresponding weight values may be assigned to the access information according to the difference in the number of accesses.
For example, there are 4 IP addresses, IP1, IP2, IP3, and IP4, respectively, and there are 3 URLs, URL1, URL2, and URL3, respectively, the number of times a page is accessed using IP1 is 5, the number of times a page is accessed using IP2 is 2, the number of times a page is accessed using IP3 is 4, the number of times a page is accessed using IP4 is 1, the number of times a page corresponding to URL1 is accessed is 3, the number of times a page corresponding to URL2 is accessed is 6, and the number of times a page corresponding to URL3 is accessed is 3.
Since the number of accesses corresponding to IP1 is greater than the number of accesses corresponding to IP3, the number of accesses corresponding to IP3 is greater than the number of accesses corresponding to IP2, and the number of accesses corresponding to IP2 is greater than the number of accesses corresponding to IP4, IP1 and IP3 can be assigned the largest weight value a, IP2 can be assigned a weight value b less than the weight value a, and IP4 can be assigned a weight value c less than the weight value b. Similarly, URL1, URL2, and URL3 may be assigned corresponding weight values.
In an alternative embodiment, fig. 2 is a schematic flow chart of allocating a corresponding weight value to access information in a method for identifying a user account, and as shown in fig. 2, the method at least includes the following steps: in step S210, if the number of accesses is greater than the number threshold, the access information is ordered according to the number of accesses to obtain an access ordering result.
The number threshold is used because the potential wool party account is a user account which frequently switches the IP address or frequently accesses the hot web page, the access information with larger access times is screened out from the access information, and then the user account can be identified as the potential wool party account to a certain extent if the user account uses the access information with larger access times.
For example, the number of times threshold is 4, and there are 5 IP addresses IPa, IPb, IPc, IPd and IPe, respectively, and the number of accesses corresponding to IPa is 10, the number of accesses corresponding to IPb is 15, the number of accesses corresponding to IPc is 6, the number of accesses corresponding to IPd is 1, the number of accesses corresponding to IPe is 20, it is apparent that only the number of accesses corresponding to IPd is not greater than the number threshold,
therefore, it is necessary to sort IPa, IPb, IPc and IPe according to the number of accesses, and the access sorting result is that the number of accesses corresponding to IPe is larger than the number of accesses corresponding to IPb, the number of accesses corresponding to IPb is larger than the number of accesses corresponding to IPa, and the number of accesses corresponding to IPa is larger than the number of accesses corresponding to IPc.
In step S220, corresponding weight values are assigned to the access information according to the access ranking result.
The access information may be assigned with a corresponding weight value according to the access ordering result, specifically, different weight values may be assigned with different access information, or the access information may be divided according to the access ordering result, and different weight values may be assigned with the access information belonging to different division results.
For example, if the access ordering result is that the number of accesses corresponding to IPe is greater than the number of accesses corresponding to IPb, the number of accesses corresponding to IPb is greater than the number of accesses corresponding to IPa, the number of accesses corresponding to IPa is greater than the number of accesses corresponding to IPc, then a weight value a may be assigned to IPe, a weight value B may be assigned to IPa, a weight value C may be assigned to IPa, and a weight value D may be assigned to IPc, where the weight value a is substantially greater than the weight value B, the weight value B is substantially greater than the weight value C, and the weight value C is substantially greater than the weight value D.
For example, if the access ranking result is that the number of accesses corresponding to URL1 is greater than the number of accesses corresponding to URL2, and the number of accesses corresponding to URL2 is greater than the number of accesses corresponding to URL3, then a weight value E may be assigned to URL1, a weight value b may be assigned to URL2, a weight value F may be assigned to URL3, and a weight value G may be assigned to URL3, where the weight value E is far greater than the weight value F, and the weight value F is far greater than the weight value G.
In this exemplary embodiment, according to the access sorting result, a corresponding weight value is allocated to the access information, so that the access information with large access times and the access information with small access times have different weight values, further, different proportions of the subsequent access vectors corresponding to different user accounts in all the access vectors are ensured, and further, the accuracy of the identified potential wool party accounts is improved.
In an alternative embodiment, fig. 3 is a schematic flow chart illustrating a method for identifying a user account and allocating a corresponding weight value to access information, where, as shown in fig. 3, the method at least includes the following steps: in step S310, the access information is divided according to the access ordering result to obtain a division result, and the access times ordering relation between different division results is determined.
The division result is a result obtained by dividing the access information according to the access ordering result, for example, there are 100 IP addresses, and the access times corresponding to the 100 access information are all greater than the time threshold, and further, the 100 IP addresses may be divided according to the access ordering result to obtain the division result, specifically, the division result includes 4 parts, the first part includes the IP address with the access times of 5 in front, the second part includes the IP address with the access times of 6 th to 10 th, the third part includes the IP address with the access times of 11 th to 20 th, the fourth part includes the IP address with the access times of 21 st to 100 th, and similarly, the URL may be divided to obtain the division result.
The access number ordering relationship refers to the access number ordering relationship between the different portions, for example, the access number ordering relationship between the four portions is that the access number of the IP address in the first portion is greater than the access number of the IP address in the second portion, the access number of the IP address in the second portion is greater than the access number of the IP address in the third portion, and the access number of the IP address in the third portion is greater than the access number of the IP address in the fourth portion, and similarly, the access number ordering relationship between the different division results corresponding to the URLs may be determined.
In step S320, different weight values are assigned to the access information belonging to different division results according to the access number ordering relationship.
According to the access frequency relation, different weight values can be allocated to the access information belonging to different division results.
For example, the division result includes 4 parts, the first part includes the IP addresses with the access times ranked in the first 5, the second part includes the IP addresses with the access times ranked in the 6 th to 10 th, the third part includes the IP addresses with the access times ranked in the 11 th to 20 th, and the fourth part is the IP addresses with the access times ranked in the 21 st to 100 th.
The access times ordering relation is that the access times of the IP addresses in the first part are larger than the access times of the IP addresses in the second part, the access times of the IP addresses in the second part are larger than the access times of the IP addresses in the third part, and the access times of the IP addresses in the third part are larger than the access times of the IP addresses in the fourth part.
Based on this, a weight H may be assigned to the IP address belonging to the first portion, a weight I may be assigned to the IP address belonging to the second portion, a weight J may be assigned to the IP address belonging to the third portion, a weight may not be assigned to the IP address belonging to the fourth portion, and the weight H is much larger than the weight I, which is much larger than the weight J.
In this exemplary embodiment, according to the access sorting result and the access frequency sorting relation, corresponding weight values are allocated to the access information belonging to different division results, so that the access information with large access frequency and the access information with small access frequency have different weight values, further, different proportions of subsequent access vectors corresponding to different user accounts in all the access vectors are ensured, and further, the accuracy of the identified potential wool party accounts is improved.
In step S130, based on the weight value and the access information, an access vector corresponding to the user account is created, so as to calculate the access vector and obtain an account similarity value between the user accounts.
In an exemplary embodiment of the present disclosure, the access vector refers to a vector corresponding to the user account, specifically, if the access information is an IP address, the access vector is a vector corresponding to the IP address, for example, if there are 5 IP addresses, i.e., IP1, IP2, IP3, IP4, and IP5, respectively, and the user account XXXa uses IP1 and IP2, then the access vector [1, 0] corresponding to the user account XXXa, and similarly, if there are 3 URLs, i.e., URL1, URL2, and URL3, respectively, the user account XXXa accesses a page corresponding to URL1, and the access vector corresponding to the user account XXXa is [1, 0].
The access vector may be calculated by using a cosine similarity formula or by using a certain algorithm, which is not particularly limited in the present exemplary embodiment.
For example, there are 200 user accounts, and the access vectors corresponding to the 200 user accounts are calculated in pairs by using the formula (1) to obtain the account similarity value.
The similarity and cos theta are account similarity values between two user accounts, and A and B are access vectors respectively corresponding to any two user accounts of 200 user accounts.
In an alternative embodiment, fig. 4 shows a schematic flow chart of creating an access vector corresponding to a user account in a user account identification method, where the access information includes an internet protocol address, and as shown in fig. 4, the method at least includes the following steps: in step S410, determining a target internet protocol address used by the user account; wherein the target internet protocol address belongs to one or more of the internet protocol addresses.
When the access information includes an IP address, in the process of creating an access vector corresponding to the user account, it is required to determine which internet protocol addresses are used by the user account, that is, it is required to determine one or more target internet protocol addresses used by the user account from all internet protocol addresses.
For example, there are 4 internet protocol addresses, IP1, IP2, IP3, and IP4, respectively, and there are 3 user accounts, user account a, user account B, and user account C, respectively, where user account a uses IP1 and IP2, user account B uses IP3, user account C uses IP1, IP3, and IP4, based on which the target network protocol addresses corresponding to user account a are IP1 and IP2, the target network protocol address corresponding to user account B is IP3, and the target network protocol addresses corresponding to user account C are IP1, IP3, and IP4.
In step S420, a first access vector corresponding to the user account is created based on the target internet protocol address and the internet protocol address.
The first access vector refers to a vector created according to the target internet protocol address and the internet protocol address, and is used for calculating an account similarity value between user accounts.
For example, the internet protocol addresses include IP1, IP2, IP3, and IP4, where IP1 and IP2 are target internet protocol addresses for user account a, and thus, a first access vector corresponding to user account a is created as [1, 0].
In step S430, a weight value corresponding to the target internet protocol address is determined, and a first access vector of weight values is created.
After the first access vector is created, a weight value is also required to be created, and the weight value corresponds to the target internet protocol address.
After creating a first access vector, a first access vector with a weight value is also created, because the weight value is a value allocated to the internet protocol address according to the number of accesses corresponding to the internet protocol address, therefore, the greater the weight value allocated to the internet protocol address, the more frequently the internet protocol address is proved to be used, and therefore, the first access vector with the weight value needs to be created to improve the duty ratio of the first access vector in the total access vector, and further improve the accuracy of identifying the potential wool party account.
For example, the internet protocol addresses include IP1, IP2, IP3, and IP4, where IP1 and IP2 are target internet protocol addresses for user account a, and thus, a first access vector corresponding to user account a is created as [1, 0].
Among them, the weight value corresponding to IP1 is 15 and the weight value corresponding to IP2 is 50, so that 65 first access vectors [1, 0] corresponding to the user account a also need to be created.
In this exemplary embodiment, a first access vector corresponding to the user account is created, a weight value corresponding to the target internet protocol address is determined, and then a first access vector with the weight value is also required to be created.
In an alternative embodiment, fig. 5 shows a schematic flow chart of creating an access vector corresponding to a user account in a user account identification method, where the access information includes a uniform resource locator, and as shown in fig. 5, the method at least includes the following steps: in step S510, determining a target uniform resource locator corresponding to the page accessed by the user account; wherein the target uniform resource locator belongs to one or more of the uniform resource locators.
The target uniform resource locator corresponds to a page accessed by the user account.
For example, when the user account a accesses the web page x1 and the web page x2, the url corresponding to the web page x1 and the url corresponding to the web page x2 are the target url.
In step S520, a second access vector corresponding to the user account is created according to the target url and url.
The second access vector refers to a vector created according to the target uniform resource locator and the uniform resource locator, and is used for calculating the account similarity value between the user accounts.
For example, there are 5 URL's, URL1, URL2, URL3, URL4, and URL5, respectively, where URL1, URL3, and URL5 are target URL's for user account a, and the second access vector created is [1,0,1,0,1].
In step S530, a weight value corresponding to the target uniform resource locator is determined, and a second access vector of weight values is created.
In addition to creating a second access vector, a second access vector with a weight value corresponding to the target uniform resource locator needs to be created.
After creating a second access vector, a weight value is also created, because the weight value is a value allocated to the uniform resource locator according to the number of accesses corresponding to the uniform resource locator, so that the larger the weight value allocated to the uniform resource locator is, the more frequently accessed the web page corresponding to the uniform resource locator is proved to be, and the weight value is also created to improve the occupation ratio of the second access vector in the total access vector, thereby improving the accuracy of identifying the potential wool party account.
For example, there are 5 URL's, URL1, URL2, URL3, URL4, and URL5, respectively, where URL1, URL3, and URL5 are target URL's for user account a, and the second access vector created is [1,0,1,0,1].
Since the weight value corresponding to URL1 is 30, the weight value corresponding to URL3 is 5, and the weight value corresponding to URL5 is 15, it is also necessary to create 50 second access vectors [1,0,1,0,1].
In this exemplary embodiment, a second access vector corresponding to the user account is created, a weight value corresponding to the target uniform resource locator is determined, and then a second access vector with a weight value is also required to be created.
In an alternative embodiment, calculating the access vector to obtain the account similarity value between the user accounts includes: and calculating the access vector by using a cosine similarity formula to obtain an account similarity value between user accounts.
The cosine similarity formula shown in the formula (1) can be used for calculating the access vector, so that account similarity values among user accounts are obtained.
For example, the first access vector corresponding to user account a is [1,0, 1], the first access vector corresponding to user account b is [0, 1], and further, according to the formula (1), calculating a first access vector [1,0, 1] and a first access vector [0, 1], wherein the obtained similarity is an account similarity value between the user account a and the user account b.
For example, the second access vector corresponding to the user account a is [1, 0], the second access vector corresponding to the user account b is [1, 1], and then the second access vector [1, 0] and the second access vector [1, 1] are calculated according to the formula (1), and the obtained similarity is the account similarity value between the user account a and the user account b.
In the present exemplary embodiment, the account similarity value between the user accounts is calculated by using the cosine similarity formula, and the account similarity value is taken as an consideration, so that the situation that the user account with abnormal single attribute information or abnormal part of attribute information is identified as the potential wool party account in the prior art is avoided, and the accuracy of the identified potential wool party account is improved.
And S140, identifying potential wool party accounts in the user accounts according to the account similarity value.
In an exemplary embodiment of the disclosure, according to the account similarity value, similar user accounts are combined into one class, and when the number of user accounts in a certain class is greater than a certain value, it is determined that the user accounts belonging to the class are potential wool party accounts.
For example, there are 1000 user accounts, and access vectors corresponding to the 1000 user accounts are calculated two by two to obtain an account similarity value between the user accounts.
And determining that the first 250 user accounts are very similar according to the account similarity value, combining the first 250 user accounts into one type, and determining the user accounts belonging to the one type as potential wool party accounts because the number of the user accounts belonging to the one type is more than 200.
In an alternative embodiment, fig. 6 is a schematic flow chart of identifying a potential wool party account in a user account identification method, and as shown in fig. 6, the method at least includes the following steps: in step S610, if the account similarity value is smaller than the first similarity threshold, two user accounts corresponding to the account similarity value are combined into one type.
The first similarity threshold refers to a limit for measuring the similarity degree between two user accounts, and when the account similarity value is smaller than the first similarity value, two user accounts corresponding to the account similarity value are proved to be similar, and the two user accounts are combined into one type.
For example, if there are 1000 user accounts and the account similarity value between the first 250 user accounts is less than the first similarity threshold, the first 250 user accounts are combined into one class.
In step S620, if the number of user accounts belonging to the class is greater than or equal to the number threshold, the user account belonging to the class is identified as a potential wool party account.
The number threshold refers to a value used for comparing the number of user accounts belonging to a certain class, and the number threshold exists because the wool party accounts usually appear in a form of a party, and further, all potential wool party accounts belonging to a party can be identified through the number threshold.
For example, if there are 1000 user accounts and the account similarity value between the first 250 user accounts is less than the first similarity threshold, then the first 250 user accounts are combined into the x-1 class.
Based on this, since the number 250 of user accounts belonging to the x-1 class is greater than the number threshold 200, the 250 user accounts belonging to the x-1 class are identified as potential wool party accounts.
In the present exemplary embodiment, on the one hand, if the account similarity value is smaller than the first similarity threshold, two user accounts corresponding to the account similarity value are combined into one class, so that the situation that in the prior art, a user account with abnormal single attribute information or abnormal part of attribute information is identified as a potential wool party account is avoided, and the accuracy of the identified potential wool party account is improved; on the other hand, if the number of the user accounts in the class is larger than the number threshold, the user accounts belonging to the class are identified as potential wool party accounts, the group characteristics of the wool party accounts are fully considered, and the accuracy of the identified potential wool party accounts is further improved.
In an alternative embodiment, fig. 7 shows a schematic flow chart of merging user accounts into one class in a user account identification method, and as shown in fig. 7, the method at least includes the following steps: in step S710, account similarity values between user accounts belonging to different classes are calculated.
If the account similarity value is smaller than the first similarity threshold, two user accounts corresponding to the account similarity value are combined into one class, and based on the two user accounts, multiple classes may appear by comparing the account similarity value with the first similarity threshold.
Further, account similarity values between user accounts belonging to different classes need to be calculated to determine whether further merging is required.
For example, by comparing the account similarity value with the first threshold, three types of user accounts are presented, including 150 user accounts belonging to the first type, 200 user accounts belonging to the second type, and 323 user accounts belonging to the third type.
Based on the above, it is necessary to perform pairwise computation on the access vectors of 150 user accounts belonging to the first class and the access vectors of 200 user accounts belonging to the second class, and also to perform pairwise computation on the access vectors of 150 user accounts belonging to the first class and the access vectors of 323 user accounts belonging to the third class, and further to perform pairwise computation on the access vectors of 200 user accounts belonging to the second class and the access vectors of 323 user accounts belonging to the third class, so as to obtain account similarity values between user accounts belonging to different classes.
In step S720, if the account similarity value is smaller than the second similarity threshold, the user accounts belonging to different classes are combined into one class.
The second similarity threshold refers to a limit for measuring whether user accounts belonging to different classes are needed to be combined, and if the account similarity value between the user accounts belonging to different classes is smaller than the second similarity threshold, the user accounts belonging to different classes are combined into one class.
For example, by comparing the account similarity value with the first threshold, three types of user accounts are presented, including 150 user accounts belonging to the first type, 200 user accounts belonging to the second type, and 323 user accounts belonging to the third type.
Based on the above, it is necessary to perform pairwise computation on the access vectors of 150 user accounts belonging to the first class and the access vectors of 200 user accounts belonging to the second class, and also to perform pairwise computation on the access vectors of 150 user accounts belonging to the first class and the access vectors of 323 user accounts belonging to the third class, and further to perform pairwise computation on the access vectors of 200 user accounts belonging to the second class and the access vectors of 323 user accounts belonging to the third class, so as to obtain account similarity values between user accounts belonging to different classes.
Because the account similarity value between the user accounts belonging to the first class and the user accounts belonging to the second class is smaller than the second similarity threshold, the user accounts in the first class and the user accounts in the second class are combined into one class.
In the present exemplary embodiment, if the account similarity between the user accounts belonging to different classes is smaller than the second similarity threshold, the user accounts belonging to different classes are combined, so that the logic of combining the user accounts is perfected, and the accuracy of the potential wool party accounts identified later is further improved.
In the method and the device provided by the exemplary embodiment of the disclosure, on one hand, the potential wool party account is identified according to the account similarity value, so that the situation that in the prior art, a user account with abnormal single attribute information or abnormal part of attribute information is identified as the wool party account is avoided; on the other hand, a weight value is distributed for the access information according to the access times, so that the duty ratio of the access vector which is accessed frequently later is increased, and the accuracy of the identified potential wool party account is improved.
The user account identification method in the embodiment of the present disclosure is described in detail below in connection with an application scenario.
Fig. 8 schematically illustrates a flow chart of a method for identifying user accounts in an application scenario, as shown in fig. 8, in which step S810 is a process of obtaining all access information and counting access times corresponding to all access information, step S820 is a process of sorting access information according to access times to obtain an access sorting result if the access times are greater than a first similarity threshold, step S830 is a process of assigning corresponding weight values to access information according to the access sorting result, step S840 is a process of creating an access vector corresponding to a user account based on the weight values and the access information, step S850 is a process of assigning corresponding weight values to access information according to the access sorting result, step S860 is a process of calculating an access vector to obtain an account similarity value between user accounts, step S870 is a process of merging two users corresponding to the account similarity value into one class if the account similarity value is less than a second similarity threshold, step S880 is a process of merging user accounts belonging to different classes into one class if the similarity value is less than the second similarity threshold, step S850 is a process of identifying user account number 890 or a potential account belonging to the class if the number of S is greater than the first similarity threshold.
In the application scene, on one hand, potential wool party accounts are identified according to the account similarity value, so that the situation that in the prior art, a user account with abnormal single attribute information or abnormal partial attribute information is identified as a wool party account is avoided; on the other hand, a weight value is distributed for the access information according to the access times, so that the duty ratio of the access vector which is accessed frequently later is increased, and the accuracy of the identified potential wool party account is improved.
In addition, in the exemplary embodiment of the disclosure, a user account identification device is also provided. Fig. 9 shows a schematic structural diagram of a user account identification apparatus, and as shown in fig. 9, the user account identification apparatus 900 may include: statistics module 910, allocation module 920, creation module 930, and identification module 940. Wherein:
a statistics module 910, configured to obtain all access information, and count the number of accesses corresponding to all access information; the allocation module 920 is configured to allocate a corresponding weight value to the access information according to the access times; the creating module 930 is configured to create an access vector corresponding to the user accounts based on the weight value and the access information, so as to calculate the access vector to obtain an account similarity value between the user accounts; the identifying device 940 is configured to identify potential wool party accounts in the user account according to the account similarity value.
The details of the above-mentioned user account identification apparatus 900 are described in detail in the corresponding user account identification method, so that they will not be described herein.
It should be noted that although several modules or units of the user account identification apparatus 900 are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. 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.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 1000 according to such an embodiment of the invention is described below with reference to fig. 10. The electronic device 1000 shown in fig. 10 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 10, the electronic device 1000 is embodied in the form of a general purpose computing device. Components of electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, a bus 1030 connecting the various system components (including the memory unit 1020 and the processing unit 1010), and a display unit 1040.
Wherein the storage unit stores program code that is executable by the processing unit 1010 such that the processing unit 1010 performs steps according to various exemplary embodiments of the present invention described in the above section of the "exemplary method" of the present specification.
The memory unit 1020 may include readable media in the form of volatile memory units such as Random Access Memory (RAM) 1021 and/or cache memory unit 1022, and may further include Read Only Memory (ROM) 1023.
Storage unit 1020 may also include a program/usage tool 1024 having a set (at least one) of program modules 1025, such program modules 1025 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which may include the reality of a network environment, or some combination thereof.
Bus 1030 may be representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 1000 can also communicate with one or more external devices 1070 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1000, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1050. Also, electronic device 1000 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 over the bus 1030. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with the electronic device 1000, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
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 in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure 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 terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
Referring to fig. 11, a program product 1100 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is 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 (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with 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 readable signal medium may also be any readable medium that is not a 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 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.
Program code for carrying out operations 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, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims (11)

1. A method for identifying a user account, the method comprising:
acquiring all access information, and counting the access times corresponding to all the access information; the access information is information used when a user accesses a page by using a user account;
distributing corresponding weight values for the access information according to the access times;
acquiring target access information used by the user when accessing a target page by using the user account, wherein the target access information is one or more of the access information;
creating an access vector corresponding to the user account according to the access information and the target access information;
Determining a weight value corresponding to the target access information, creating an access vector of the weight value, and calculating the access vector by using a cosine similarity formula to obtain an account similarity value between the user accounts;
and identifying potential wool party accounts in the user accounts according to the account similarity value.
2. A method of identifying a user account according to claim 1, wherein the access information includes an internet protocol address and a uniform resource locator corresponding to a page accessed by the user account.
3. The method for identifying a user account according to claim 2, wherein the assigning a corresponding weight value to the access information according to the number of accesses includes:
if the access times are greater than a times threshold, sorting the access information according to the access times to obtain an access sorting result;
and distributing corresponding weight values for the access information according to the access sequencing result.
4. A method for identifying a user account according to claim 3, wherein the assigning a corresponding weight value to the access information according to the access ranking result comprises:
Dividing the access information according to the access ordering result to obtain a dividing result, and determining access times ordering relation among different dividing results;
and according to the access times ordering relation, different weight values are allocated to the access information belonging to different division results.
5. The user account identification method of claim 2, wherein the access information comprises the internet protocol address;
the method further comprises the steps of:
determining a target internet protocol address used by the user account; wherein the target internet protocol address belongs to one or more of the internet protocol addresses;
creating a first access vector corresponding to the user account according to the target internet protocol address and the internet protocol address;
and determining a weight value corresponding to the target internet protocol address, and creating the weight value and the first access vector.
6. The user account identification method of claim 2, wherein the access information comprises the uniform resource locator;
the method further comprises the steps of:
determining a target uniform resource locator corresponding to the page accessed by the user account; wherein the target uniform resource locator belongs to one or more of the uniform resource locators;
Creating a second access vector corresponding to the user account according to the target uniform resource locator and the uniform resource locator;
and determining a weight value corresponding to the target uniform resource locator, and creating the weight value and the second access vector.
7. The method for identifying a user account according to any one of claims 5 or 6, wherein the step of identifying a potential wool party account from the user account according to the account similarity value comprises:
if the account similarity value is smaller than a first similarity threshold, combining two user accounts corresponding to the account similarity value into one type;
and if the number of the user accounts belonging to the class is greater than or equal to a number threshold, identifying the user accounts belonging to the class as potential wool party accounts.
8. A method of user account identification as claimed in claim 7, further comprising:
calculating account similarity values between the user accounts belonging to different classes;
and if the account similarity value is smaller than a second similarity threshold value, merging the user accounts belonging to different classes into one class.
9. A user account identification device applied to a base station, comprising:
the statistics module is configured to acquire all access information and count the access times corresponding to all the access information; the access information is information used when a user accesses a page by using a user account;
the distribution module is configured to distribute corresponding weight values for the access information according to the access times;
the creating device is configured to acquire target access information used when the user accesses a target page by using the user account, wherein the target access information is one or more of the access information; creating an access vector corresponding to the user account according to the access information and the target access information; determining a weight value corresponding to the target access information, creating an access vector of the weight value, and calculating the access vector by using a cosine similarity formula to obtain an account similarity value between the user accounts;
and the identification device is configured to identify potential wool party accounts in the user accounts according to the account similarity value.
10. An electronic device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the user account identification method of any of claims 1-8 via execution of the executable instructions.
11. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the user account identification method of any of claims 1-8.
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CN105528352A (en) * 2014-09-29 2016-04-27 国际商业机器公司 Method for establishing corresponding relation of mobile communication user and network account information thereof
CN110460587A (en) * 2019-07-23 2019-11-15 平安科技（深圳）有限公司 A kind of exception account detection method, device and computer storage medium
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Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105528352A (en) * 2014-09-29 2016-04-27 国际商业机器公司 Method for establishing corresponding relation of mobile communication user and network account information thereof
CN110460587A (en) * 2019-07-23 2019-11-15 平安科技（深圳）有限公司 A kind of exception account detection method, device and computer storage medium
CN114257427A (en) * 2021-12-09 2022-03-29 北京知道创宇信息技术股份有限公司 Target user identification method and device, electronic equipment and storage medium

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