Cheating user identification method, device, electronic device and storage medium

Abstract

The embodiment of the present invention relates to a cheating user identification method, device, electronic device and storage medium, the method comprising: obtaining feature data of multiple first users, the feature data including the geographic location of the first users when participating in a network activity initiated by a second user; based on the feature data of the multiple first users, determining whether the second user is a cheating user, the cheating user refers to a user who initiates the network activity in a cheating manner. Thus, a more comprehensive and accurate identification of cheating users can be achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of computer technology, and in particular to a cheating user identification method, device, electronic device, and storage medium. Background Art

[0002] At present, many online platforms set up new user activities in order to attract new users. For example, old users invite new users. After the new users complete the registration, the old users can get rewards. In such activities, in order to get more rewards faster, old users can use real-person crowdsourcing to complete the new user acquisition task. Real-person crowdsourcing means that users post tasks on some online platforms (hereinafter referred to as real-person crowdsourcing platforms), and other users help them complete the task and get rewards. However, the above-mentioned users completing the new user acquisition task through real-person crowdsourcing is essentially a cheating behavior, so such users can be called cheating users.

[0003] In the prior art, in order to identify cheating users, a method is proposed to capture intelligence from the above-mentioned live crowdsourcing platform through an undercover crawler, such as invitation codes, invitation links and other information appearing on the live crowdsourcing platform, and parse and match the captured intelligence to identify cheating users.

[0004] However, the above methods are unable to fully identify cheating users due to limited intelligence capture. Summary of the invention

[0005] In view of this, in order to solve the above-mentioned technical problem that cheating users cannot be fully identified due to limited intelligence capture, the embodiments of the present invention provide a cheating user identification method, device, electronic device and storage medium.

[0006] In a first aspect, an embodiment of the present invention provides a cheating user identification method, the method comprising:

[0007] Acquire characteristic data of a plurality of first users, the characteristic data including geographic locations of the first users when participating in a network activity initiated by a second user;

[0008] Based on the feature data of the plurality of first users, it is determined whether the second user is a cheating user, where the cheating user refers to a user who initiates the network activity in a cheating manner.

[0009] In a possible implementation, the characteristic data further includes: behavior data of the first user after participating in a network activity initiated by the second user;

[0010] The determining whether the second user is a cheating user based on the feature data of the plurality of first users includes:

[0011] Based on the geographical locations corresponding to the plurality of first users, determining whether the second user is a suspected cheating user;

[0012] In the case where it is determined that the second user is a suspected cheating user, whether the second user is a cheating user is determined based on the behavior data corresponding to a plurality of the first users.

[0013] In a possible implementation manner, determining whether the second user is a cheating user based on the feature data of the plurality of first users includes:

[0014] Determining a geographic dispersion among the plurality of first users based on the geographic locations corresponding to the plurality of first users;

[0015] comparing the geographic dispersion with a preset dispersion threshold;

[0016] If the geographic dispersion is greater than the dispersion threshold, the second user is determined to be a cheating user; if the geographic dispersion is less than or equal to the dispersion threshold, the second user is determined not to be a cheating user.

[0017] In a possible implementation, determining the geographic dispersion among the plurality of first users based on the geographic locations corresponding to the plurality of first users includes:

[0018] Dividing the plurality of first users into a plurality of groups, wherein first users corresponding to the same geographical location are divided into the same group, and first users corresponding to different geographical locations are divided into different groups;

[0019] Determine a ratio of the number of first users in each of the groups to the total number of the first users;

[0020] Substitute the ratio corresponding to each of the groups into a preset geographic dispersion calculation formula to obtain the geographic dispersion among the plurality of the first users.

[0021] In a possible implementation, the determining whether the second user is a cheating user based on the behavior data corresponding to the plurality of first users includes:

[0022] Determining retention rates of the plurality of first users based on the behavior data corresponding to the plurality of first users;

[0023] Comparing the retention rate with a preset retention rate threshold;

[0024] If the retention rate is less than the retention rate threshold, the second user is determined to be a cheating user; if the retention rate is greater than or equal to the retention rate threshold, the second user is determined not to be a cheating user.

[0025] In a possible implementation, determining the retention rates of the plurality of first users based on the behavior data corresponding to the plurality of first users includes:

[0026] Determining, based on the behavior data corresponding to the plurality of first users, the activity levels corresponding to the plurality of first users;

[0027] Determine, from the plurality of first users, a target user whose activity level is greater than a preset activity level threshold;

[0028] A ratio of the number of the target users to the total number of the first users is determined, and the ratio is determined as the retention degree of the plurality of the first users.

[0029] In a possible implementation manner, after determining that the second user is a cheating user, the method further includes:

[0030] When receiving the network activity initiation request from the second user, intercepting the network activity initiation request.

[0031] In a possible implementation manner, after determining that the second user is a cheating user, the method further includes:

[0032] When receiving a resource acquisition request from the second user, the resource acquisition request is intercepted, where the resource acquisition request is used to request to obtain resources corresponding to any one of the first user's participation in the network activity initiated by the second user.

[0033] In a second aspect, an embodiment of the present invention provides a cheating user identification device, the device comprising:

[0034] an acquisition module, configured to acquire characteristic data of a plurality of first users, wherein the characteristic data includes geographical locations of the first users when they participate in a network activity initiated by a second user;

[0035] The determination module is used to determine whether the second user is a cheating user based on the feature data of the plurality of the first users, wherein the cheating user refers to a user who initiates the network activity in a cheating manner.

[0036] In a possible implementation, the determining module includes:

[0037] A first determination submodule, configured to determine whether the second user is a suspected cheating user based on the geographical locations corresponding to the plurality of first users, when the characteristic data further includes: behavior data of the first user after participating in the network activity initiated by the second user;

[0038] The second determination submodule is used to determine whether the second user is a suspected cheating user based on the behavior data corresponding to a plurality of the first users when the second user is determined to be a suspected cheating user.

[0039] In a possible implementation, the determining module includes:

[0040] A third determining submodule, configured to determine the geographic dispersion between the plurality of first users based on the geographic locations corresponding to the plurality of first users;

[0041] A comparison submodule, used for comparing the geographic dispersion with a preset dispersion threshold;

[0042] a fourth determination submodule, configured to determine that the second user is a cheating user if the geographical dispersion is greater than the dispersion threshold;

[0043] The fifth determination submodule is configured to determine that the second user is not a cheating user if the geographical dispersion is less than or equal to the dispersion threshold.

[0044] In a possible implementation manner, the third determining submodule is specifically configured to:

[0045] Dividing the plurality of first users into a plurality of groups, wherein first users corresponding to the same geographical location are divided into the same group, and first users corresponding to different geographical locations are divided into different groups;

[0046] Determine a ratio of the number of first users in each of the groups to the total number of the first users;

[0047] Substitute the ratio corresponding to each of the groups into a preset geographic dispersion calculation formula to obtain the geographic dispersion among the plurality of the first users.

[0048] In a possible implementation manner, the second determining submodule includes:

[0049] A first determining unit, configured to determine retention degrees of a plurality of the first users based on the behavior data corresponding to the plurality of the first users;

[0050] A comparing unit, configured to compare the retention rate with a preset retention rate threshold;

[0051] a second determining unit, configured to determine that the second user is a cheating user if the retention rate is less than the retention rate threshold;

[0052] The third determining unit is configured to determine that the second user is not a cheating user if the retention rate is greater than or equal to the retention rate threshold.

[0053] In a possible implementation manner, the first determining unit is specifically configured to:

[0054] Determining, based on the behavior data corresponding to the plurality of first users, the activity levels corresponding to the plurality of first users;

[0055] Determine, from the plurality of first users, a target user whose activity level is greater than a preset activity level threshold;

[0056] A ratio of the number of the target users to the total number of the first users is determined, and the ratio is determined as the retention degree of the plurality of the first users.

[0057] In a possible implementation, the device further includes:

[0058] The first interception module is used to intercept the network activity initiation request when receiving the network activity initiation request of the second user after determining that the second user is a cheating user.

[0059] In a possible implementation, the device further includes:

[0060] The second interception module is used to intercept the resource acquisition request when receiving the resource acquisition request of the second user after determining that the second user is a cheating user, and the resource acquisition request is used to request to obtain the resources corresponding to any network activity initiated by the second user in which the first user participates.

[0061] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory, wherein the processor is used to execute a cheating user identification program stored in the memory to implement the cheating user identification method described in any one of the first aspects.

[0062] In a fourth aspect, an embodiment of the present invention provides a storage medium, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the cheating user identification method described in any one of the first aspects.

[0063] The technical solution provided by the embodiment of the present invention obtains characteristic data of multiple first users, wherein the characteristic data includes the geographical location of the first user when participating in the network activity initiated by the second user. Then, based on the characteristic data of the multiple first users, it can be determined whether the second user is a cheating user, and a cheating user refers to a user who initiates the network activity in a cheating manner. Since the determination of whether the second user is a cheating user is based on the characteristic data of multiple first users participating in the network activity initiated by the second user, that is to say, when a user initiates a network activity and other users participate in the network activity, the technical solution provided by the embodiment of the present invention can be applied to identify whether the user is a cheating user. Therefore, compared with the prior art of identifying cheating users by capturing intelligence, cheating users can be identified more comprehensively. Moreover, since the geographical location of the first user when participating in the network activity initiated by the second user can reflect the means or means adopted by the second user to initiate the network activity from the side, cheating users can be identified more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of an embodiment of a cheating user identification method provided by an embodiment of the present invention;

[0065] Figure 2 A flowchart of another cheating user identification method provided by an embodiment of the present invention;

[0066] Figure 3 A block diagram of an embodiment of a cheating user identification device provided by an embodiment of the present invention;

[0067] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] To facilitate understanding of the embodiments of the present invention, the application scenarios involved in the embodiments of the present invention are first exemplarily described below.

[0070] At present, many network platforms or APPs (Application, mobile software) set up new user activities in order to attract new users. The new user activities are to stimulate users to share and forward by obtaining corresponding rewards by completing corresponding tasks, so as to quickly attract new users.

[0071] In the process of old users inviting new users, some old users often use real-person crowdsourcing to complete the new user acquisition task in order to obtain more rewards faster. For example, old users post new user acquisition tasks on some online platforms (hereinafter referred to as real-person crowdsourcing platforms), such as downloading **APP and completing registration. Other users on the real-person crowdsourcing platform can help the old user complete this task.

[0072] However, the above-mentioned users' behavior of completing the task of attracting new customers through real-person crowdsourcing is essentially a cheating behavior, and therefore, such users can be called cheating users.

[0073] In the prior art, in order to identify cheating users who use real-person crowdsourcing, technicians have proposed a method of using undercover crawlers to capture intelligence, such as invitation codes, invitation links and other information that appear on real-person crowdsourcing platforms, and parsing and matching the captured intelligence to identify cheating users.

[0074] However, in the above method for identifying cheating users, due to limited captured intelligence, it is impossible to fully identify cheating users using real-person crowdsourcing. In this regard, the present invention proposes a cheating user identification method to solve the technical problem that cheating users cannot be fully identified in the prior art.

[0075] The cheating user identification method provided by the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.

[0076] See also Figure 1 , is a flow chart of an embodiment of a cheating user identification method provided by an embodiment of the present invention. As an embodiment, Figure 1 The process shown can be applied to the server. Figure 1 As shown, the process may include the following steps:

[0077] Step 101: Acquire characteristic data of a plurality of first users, wherein the characteristic data includes geographic locations of the first users when they participate in a network activity initiated by a second user.

[0078] The second user is the initiator of the network activity, and is also the user to be identified in the embodiment of the present invention as to whether the network activity is initiated in a cheating manner. Here, the network activity may be, for example, a new member acquisition activity, a sharing activity, a support activity, etc., and initiating a network activity in a cheating manner may be, for example, initiating a network activity in the form of real-person crowdsourcing.

[0079] In an example, suppose user A sends a download link of client 2 to user B's client 1 through client 1 (such as WeChat, QQ, Money Maker, etc.), and suppose user B clicks on the link, installs client 2 on the terminal device, and registers as a new user. Then, user A can be regarded as the second user, and user B can be regarded as the first user who participates in the network activity initiated by the second user. That is, the first user refers to the participant of the network activity.

[0080] In practice, when a first user registers as a new user on a client, the server of the client may collect characteristic data of the first user. Here, the characteristic data of the first user may include but is not limited to: geographical locations of multiple first users when participating in a network activity initiated by a second user.

[0081] Based on this, in the embodiment of the present invention, when the server performs cheating user identification, the geographic locations of multiple first users when participating in the network activities initiated by the second user can be obtained. Specifically, the IP (Internet Protocol) addresses of multiple first users when participating in the network activities initiated by the second user can be obtained, and the geographic location corresponding to the first user can be determined according to the IP address of the first user.

[0082] Step 102: Based on the feature data of the plurality of first users, determine whether the second user is a cheating user, where the cheating user refers to a user who initiates network activities in a cheating manner.

[0083] From the introduction of step 102, it can be seen that the above-mentioned cheating user initiates the network activity in a cheating manner, such as posting a task through a real-person crowdsourcing platform, and other users can receive rewards by helping the user complete the task. The user who initiates the network activity in this real-person crowdsourcing manner can be called a cheating user.

[0084] As can be seen from the introduction of step 101, the characteristic data of the plurality of first users may include but are not limited to: the geographical locations of the plurality of first users when they participate in the network activity initiated by the second user.

[0085] Based on this, in one embodiment, the server may determine the geographic dispersion among the multiple first users based on the geographic locations corresponding to the multiple first users, and determine whether the second user is a cheating user according to the geographic dispersion among the multiple first users.

[0086] Specifically, when determining the geographic dispersion among multiple first users based on the geographic locations of multiple first users when they participate in network activities initiated by second users, its specific implementation may include the following steps: first, dividing the multiple first users to obtain multiple groups, wherein first users corresponding to the same geographic location are divided into the same group, and first users corresponding to different geographic locations are divided into different groups.

[0087] Then, the ratio of the number of first users in each group to the total number of first users is determined. Then, the ratio corresponding to each group is substituted into the preset geographic dispersion calculation formula shown in the following formula (1) to obtain the geographic dispersion among the first users.

[0088]

[0089] In the above formula (1), G is the geographic dispersion among the multiple first users, p is the ratio of the number of first users in each group to the total number of the multiple first users, and K is the number of the above groups. That is, when K is 1, it means that the multiple first users are divided into one group, and the multiple first users are from the same geographical location. k represents the group number in the above group. For example, when K is 5, it means that the multiple first users are divided into 5 groups, and k is 2, which represents the second group.

[0090] For example, assuming that the total number of first users is 10, of which 3 first users are located in Shanghai when participating in the network activities initiated by the second user, 3 first users are located in Beijing when participating in the network activities initiated by the second user, and the other 4 first users are located in Tianjin when participating in the network activities initiated by the second user. Therefore, the above 10 first users can be divided into three groups, with first users in Shanghai forming one group, first users in Beijing forming one group, and first users in Tianjin forming one group. Then, the ratio of the number of first users in each group to the total number of multiple first users is:

[0091] Afterwards, the ratio corresponding to each group is respectively substituted into the geographic dispersion calculation formula shown in formula (1), and it can be obtained that the geographic dispersion among the multiple first users is 0.66.

[0092] Finally, the geographic dispersion between multiple first users can be compared with a preset dispersion threshold. If the geographic dispersion is greater than the dispersion threshold, it means that the geographic locations of multiple first users are relatively dispersed when participating in the network activities initiated by the second user. The first users registered through real-person crowdsourcing are characterized in that the first users are not people around the second user, but random users from all over the country, and their geographic locations are relatively dispersed. Therefore, the second user can be determined to be a cheating user; if the geographic dispersion is less than or equal to the dispersion threshold, it means that the geographic locations of multiple first users are relatively concentrated when participating in the network activities initiated by the second user, and they may be people around the second user. Therefore, it can be determined that the second user is not a cheating user.

[0093] The technical solution provided by the embodiment of the present invention obtains characteristic data of multiple first users, wherein the characteristic data includes the geographical location of the first user when participating in the network activity initiated by the second user. Then, based on the characteristic data of the multiple first users, it can be determined whether the second user is a cheating user, and a cheating user refers to a user who initiates the network activity in a cheating manner. Since the determination of whether the second user is a cheating user is based on the characteristic data of multiple first users participating in the network activity initiated by the second user, that is to say, when a user initiates a network activity and other users participate in the network activity, the technical solution provided by the embodiment of the present invention can be applied to identify whether the user is a cheating user. Therefore, compared with the prior art of identifying cheating users by capturing intelligence, cheating users can be identified more comprehensively. Moreover, since the geographical location of the first user when participating in the network activity initiated by the second user can reflect the means or means adopted by the second user to initiate the network activity from the side, cheating users can be identified more accurately.

[0094] In addition, due to the technical solution provided by the embodiment of the present invention, whether the second user is a cheating user is determined by using the feature data of multiple first users who participate in the network activity initiated by the second user. If the second user is determined to be a cheating user, the feature data of the multiple first users can be used as a complete chain of evidence, which is easily accepted by the activity party, thereby enhancing the explainability of detecting cheating users.

[0095] See also Figure 2 , is a flow chart of another embodiment of a cheating user identification method provided by an embodiment of the present invention. Figure 2 As shown, the process may include the following steps:

[0096] Step 201: Acquire characteristic data of a plurality of first users, wherein the characteristic data includes geographic locations of the first users when participating in a network activity initiated by a second user and behavior data of the first users after participating in the network activity initiated by the second user.

[0097] Figure 2 The process shown in the above Figure 1 Based on the process shown, the characteristic data of the first user may also include the behavior data of the first user after participating in the network activity initiated by the second user. In practice, after the first user registers as a new user on the client, when the first user uses the service provided by the client, the server of the client may collect the behavior data of the first user. Here, the behavior data may include but is not limited to: usage time, usage frequency, usage duration, etc.

[0098] Specifically, the server may determine the behavior data of the multiple first users after participating in the network activity initiated by the second user by acquiring the behavior logs of the multiple first users.

[0099] Step 202: based on the geographic locations of the plurality of first users when participating in the network activity initiated by the second user, determine whether the second user is a suspected cheating user. If so, execute step 203; if not, end the process.

[0100] The above-mentioned suspected cheating user means that the second user may be a cheating user or may not be a cheating user, and further confirmation is still needed.

[0101] In one embodiment, when determining whether a second user is a cheating user based on feature data of a plurality of first users, it may be first determined whether the second user is a suspected cheating user based on geographic locations corresponding to the plurality of first users.

[0102] Specifically, the geographic dispersion among the multiple first users can be determined based on the geographic locations corresponding to the multiple first users (the specific determination process can be found in the description of step 102, which will not be repeated here). After that, the geographic dispersion among the multiple first users is compared with the preset dispersion threshold. If the geographic dispersion is greater than the dispersion threshold, it means that the geographic locations of the multiple first users when participating in the network activities initiated by the second user are relatively dispersed, and the first users registered through real-person crowdsourcing are characterized in that the first users are not people around the second user, but random users from all over the country, and their geographic locations are relatively dispersed. Therefore, the second user can be determined to be a suspected cheating user; if the geographic dispersion is less than or equal to the dispersion threshold, it means that the geographic locations of the multiple first users when participating in the network activities initiated by the second user are relatively concentrated, and they may be people around the second user, so it can be determined that the second user is not a suspected cheating user.

[0103] Step 203: Determine whether the second user is a cheating user based on the behavior data of the plurality of first users after participating in the network activity initiated by the second user.

[0104] In one embodiment, when it is determined that the second user is a suspected cheating user, it may be further determined whether the second user is a cheating user based on the behavior data corresponding to the plurality of first users.

[0105] Specifically, the retention rates of the multiple first users may be determined based on the behavioral data corresponding to the multiple first users. The retention rate refers to the ratio of the number of first users who remain active after participating in the network activities initiated by the second user to the total number of first users.

[0106] Specifically, the retention rate of multiple first users can be determined through the following steps: First, based on the behavioral data of multiple first users, the activity levels corresponding to the multiple first users are determined. The activity level here refers to the number of times multiple first users are active within a preset time period after participating in a network activity initiated by a second user. For example, after user A registers for an APP through user B, the number of active times of the user increases by 1 every time the login access time exceeds 10 minutes.

[0107] Then, from the multiple first users, target users whose corresponding activity is greater than a preset activity threshold (for example, the activity threshold is 10) are determined. Finally, the ratio of the number of target users to the total number of first users is determined, and the above ratio is determined as the retention of the multiple first users.

[0108] For example, assuming that the total number of first users is 20, and assuming that in the past month, 5 of the first users have an active count of 1, 6 of the first users have an active count of 2, 6 of the first users have an active count of 4, and 3 of the first users have an active count of 6. Continuing to assume that when the activity of the first user is greater than or equal to 2, the first user is determined to be a target user. Based on this, there are 15 target users among the above 20 first users, and the ratio of the number of target users to the total number of first users, that is, the retention of the above 20 first users, is 0.75.

[0109] Afterwards, the retention rates of the multiple first users are compared with a preset retention threshold. If the retention rate is less than the retention threshold, it means that the activity levels of the multiple first users are relatively low, that is, after the multiple first users complete registration of the client, the subsequent usage rate is relatively low. In real-person crowdsourcing behavior, the multiple first users may register in order to obtain the rewards issued by the second user, and no longer use it in the later period. Therefore, it can be determined that the second user is a cheating user; if the retention rate is greater than or equal to the retention threshold, it means that the retention rates of the multiple first users are relatively high, that is, after completing the registration of the client, the frequency of using the client in the later period is relatively high. Therefore, it can be determined that the second user is not a cheating user.

[0110] In addition, when acquiring the characteristic data of the above-mentioned multiple first users, multiple time windows may be selected for statistics, and different time windows may correspond to different dispersion thresholds and retention thresholds, respectively.

[0111] For example, assuming that there are 30 days in a month, when counting the characteristic data of multiple first users in the month, the characteristic data of multiple first users corresponding to the time window from the 1st to the 8th of the month can be counted; the characteristic data of multiple first users corresponding to the time window from the 9th to the 15th of the month can be counted on the 15th of the month; and the characteristic data of multiple first users corresponding to the time window from the 16th to the 30th of the month can be counted on the 30th of the month.

[0112] Based on this, we continue to assume that the dispersion threshold corresponding to the time window from the 1st to the 8th of the month is G1, and the retention threshold is L1; the dispersion threshold corresponding to the time window from the 9th to the 15th of the month is G2, and the retention threshold is L2; ​​the dispersion threshold corresponding to the time window from the 16th to the 30th of the month is G3, and the retention threshold is L3. Then, the above G1, G2, and G3 may be the same dispersion threshold or different dispersion thresholds, and the above retention thresholds L1, L2, and L3 may be the same retention threshold or different retention thresholds, and the present invention does not impose any limitation on this.

[0113] The technical solution provided by the embodiment of the present invention obtains characteristic data of multiple first users, the characteristic data includes the geographical location of the first user when participating in the network activity initiated by the second user and the behavioral data of the first user after participating in the network activity initiated by the second user, and then determines whether the second user is a suspected cheating user based on the geographical location, and in the case where the second user is determined to be a suspected cheating user, determines whether the second user is a cheating user based on the behavioral data. Since the judgment of whether the second user is a cheating user is based on the geographical location of multiple first users participating in the network activity initiated by the second user and the behavioral data after participating, compared with the prior art of identifying cheating users by capturing intelligence, cheating users can be more comprehensively identified from multiple aspects, and since the geographical location of the first user can reflect the means or means used by the second user to initiate the network activity from the side, the behavioral data of the first user after participating in the network activity can reflect from the side whether the first user has a real application intention for the service corresponding to the network activity, so cheating users can be identified more accurately.

[0114] In addition, in one embodiment, when it is determined that the second user is a cheating user, if a resource acquisition request from the second user is received, the resource acquisition request can be intercepted, where the resource acquisition request is used to request to obtain resources corresponding to any network activity initiated by the second user in which the first user participates.

[0115] In one embodiment, when it is determined that the second user is a cheating user, if a network activity initiation request from the second user is received, that is, when the second user initiates a network activity again, the network activity initiation request initiated by the second user can be intercepted to effectively protect resources.

[0116] See also Figure 3 , is a block diagram of an embodiment of a cheating user identification device provided by an embodiment of the present invention. Figure 3 As shown, the device comprises:

[0117] An acquisition module 31, configured to acquire characteristic data of a plurality of first users, wherein the characteristic data includes geographic locations of the first users when they participate in a network activity initiated by a second user;

[0118] The determination module 32 is used to determine whether the second user is a cheating user based on the feature data of the plurality of first users, where the cheating user refers to a user who initiates the network activity in a cheating manner.

[0119] In a possible implementation, the determination module 32 includes (not shown in the figure):

[0120] A first determination submodule, configured to determine whether the second user is a suspected cheating user based on the geographical locations corresponding to the plurality of first users, when the characteristic data further includes: behavior data of the first user after participating in the network activity initiated by the second user;

[0121] The second determination submodule is used to determine whether the second user is a suspected cheating user based on the behavior data corresponding to a plurality of the first users when the second user is determined to be a suspected cheating user.

[0122] In a possible implementation, the determination module 32 includes (not shown in the figure):

[0123] A third determining submodule, configured to determine the geographic dispersion between the plurality of first users based on the geographic locations corresponding to the plurality of first users;

[0124] A comparison submodule, used for comparing the geographic dispersion with a preset dispersion threshold;

[0125] a fourth determination submodule, configured to determine that the second user is a cheating user if the geographical dispersion is greater than the dispersion threshold;

[0126] The fifth determination submodule is configured to determine that the second user is not a cheating user if the geographical dispersion is less than or equal to the dispersion threshold.

[0127] In a possible implementation manner, the third determining submodule is specifically configured to:

[0128] Dividing the plurality of first users into a plurality of groups, wherein first users corresponding to the same geographical location are divided into the same group, and first users corresponding to different geographical locations are divided into different groups;

[0129] Determine a ratio of the number of first users in each of the groups to the total number of the first users;

[0130] Substitute the ratio corresponding to each of the groups into a preset geographic dispersion calculation formula to obtain the geographic dispersion among the plurality of the first users.

[0131] In a possible implementation manner, the second determining submodule includes (not shown in the figure):

[0132] A first determining unit, configured to determine retention degrees of a plurality of the first users based on the behavior data corresponding to the plurality of the first users;

[0133] A comparing unit, configured to compare the retention rate with a preset retention rate threshold;

[0134] a second determining unit, configured to determine that the second user is a cheating user if the retention rate is less than the retention rate threshold;

[0135] The third determining unit is configured to determine that the second user is not a cheating user if the retention rate is greater than or equal to the retention rate threshold.

[0136] In a possible implementation manner, the first determining unit is specifically configured to:

[0137] Determining, based on the behavior data corresponding to the plurality of first users, the activity levels corresponding to the plurality of first users;

[0138] Determine, from the plurality of first users, a target user whose activity level is greater than a preset activity level threshold;

[0139] A ratio of the number of the target users to the total number of the first users is determined, and the ratio is determined as the retention degree of the plurality of the first users.

[0140] In a possible implementation, the device further includes (not shown in the figure):

[0141] The first interception module is used to intercept the network activity initiation request when receiving the network activity initiation request of the second user after determining that the second user is a cheating user.

[0142] In a possible implementation, the device further includes (not shown in the figure):

[0143] The second interception module is used to intercept the resource acquisition request when receiving the resource acquisition request of the second user after determining that the second user is a cheating user, and the resource acquisition request is used to request to obtain the resources corresponding to any network activity initiated by the second user in which the first user participates.

[0144] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, Figure 4 The electronic device 400 shown includes: at least one processor 401, a memory 402, at least one network interface 404 and a user interface 403. The various components in the electronic device 400 are coupled together via a bus system 405. It is understood that the bus system 405 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 405 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 405 is not shown in FIG. Figure 4 Various buses are labeled as bus system 405 .

[0145] The user interface 403 may include a display, a keyboard, or a pointing device (eg, a mouse, a trackball, a touch pad, or a touch screen).

[0146] It can be understood that the memory 402 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 402 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0147] In some implementations, the memory 402 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 4021 and application programs 4022 .

[0148] The operating system 4021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 4022 includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application 4022.

[0149] In the embodiment of the present invention, by calling the program or instruction stored in the memory 402, specifically, the program or instruction stored in the application 4022, the processor 401 is used to execute the method steps provided by each method embodiment, for example, including:

[0150] Acquire characteristic data of a plurality of first users, the characteristic data including geographic locations of the first users when participating in a network activity initiated by a second user;

[0151] Based on the feature data of the plurality of first users, it is determined whether the second user is a cheating user, where the cheating user refers to a user who initiates the network activity in a cheating manner.

[0152] The method disclosed in the above embodiment of the present invention can be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 401 or the instruction in the form of software. The above processor 401 can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or the hardware and software units in the decoding processor can be executed. The software unit can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and completes the steps of the above method in combination with its hardware.

[0153] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0154] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0155] The electronic device provided in this embodiment may be Figure 4 The electronic device shown in FIG. 1 may perform the following steps: Figures 1-2 All steps of the cheating user identification method in Figures 1-2 For details, please refer to the technical effect of the cheating user identification method shown in Figures 1-2 For the sake of brevity, the relevant description is not repeated here.

[0156] The embodiment of the present invention also provides a storage medium (computer-readable storage medium). The storage medium here stores one or more programs. The storage medium may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk or a solid-state drive; the memory may also include a combination of the above-mentioned types of memory.

[0157] When one or more programs in the storage medium can be executed by one or more processors, the cheating user identification method executed on the electronic device side can be implemented.

[0158] The processor is used to execute the cheating user identification program stored in the memory to implement the following steps of the cheating user identification method executed on the electronic device side:

[0159] Acquire characteristic data of a plurality of first users, the characteristic data including geographic locations of the first users when participating in a network activity initiated by a second user;

[0160] Based on the feature data of the plurality of first users, it is determined whether the second user is a cheating user, where the cheating user refers to a user who initiates the network activity in a cheating manner.

[0161] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0162] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0163] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cheating user identification method, characterized in that: The method comprises: Acquire characteristic data of a plurality of first users, the characteristic data including geographic locations of the first users when participating in a network activity initiated by a second user; Based on the feature data of the plurality of first users, determining whether the second user is a cheating user, wherein the cheating user refers to a user who initiates the network activity in a cheating manner; The determining whether the second user is a cheating user based on the feature data of the plurality of first users includes: Determining a geographic dispersion among the plurality of first users based on the geographic locations corresponding to the plurality of first users; comparing the geographic dispersion with a preset dispersion threshold; If the geographic dispersion is greater than the dispersion threshold, the second user is determined to be a cheating user; if the geographic dispersion is less than or equal to the dispersion threshold, the second user is determined not to be a cheating user.

2. The method according to claim 1, characterized in that The characteristic data also includes: behavior data of the first user after participating in the network activity initiated by the second user; The determining whether the second user is a cheating user based on the feature data of the plurality of first users includes: Based on the geographical locations corresponding to the plurality of first users, determining whether the second user is a suspected cheating user; In the case where it is determined that the second user is a suspected cheating user, whether the second user is a cheating user is determined based on the behavior data corresponding to a plurality of the first users.

3. The method according to claim 1, characterized in that The determining, based on the geographical locations corresponding to the plurality of first users, the geographical dispersion among the plurality of first users comprises: Dividing the plurality of first users into a plurality of groups, wherein first users corresponding to the same geographical location are divided into the same group, and first users corresponding to different geographical locations are divided into different groups; Determine a ratio of the number of first users in each of the groups to the total number of the first users; Substitute the ratio corresponding to each of the groups into a preset geographic dispersion calculation formula to obtain the geographic dispersion among the plurality of the first users.

4. The method according to claim 2, characterized in that: The determining whether the second user is a cheating user based on the behavior data corresponding to the plurality of first users includes: Determining retention rates of the plurality of first users based on the behavior data corresponding to the plurality of first users; Comparing the retention rate with a preset retention rate threshold; If the retention rate is less than the retention rate threshold, the second user is determined to be a cheating user; if the retention rate is greater than or equal to the retention rate threshold, the second user is determined not to be a cheating user.

5. The method according to claim 4, characterized in that The determining, based on the behavior data corresponding to the plurality of first users, the retention rates of the plurality of first users comprises: Determining, based on the behavior data corresponding to the plurality of first users, the activity levels corresponding to the plurality of first users; Determine, from the plurality of first users, a target user whose activity level is greater than a preset activity level threshold; A ratio of the number of the target users to the total number of the first users is determined, and the ratio is determined as the retention degree of the plurality of the first users.

6. The method according to claim 1, characterized in that After determining that the second user is a cheating user, the method further includes: When receiving the network activity initiation request from the second user, intercepting the network activity initiation request.

7. The method according to claim 1, characterized in that After determining that the second user is a cheating user, the method further includes: When receiving a resource acquisition request from the second user, the resource acquisition request is intercepted, where the resource acquisition request is used to request to obtain resources corresponding to any one of the first user's participation in the network activity initiated by the second user.

8. A cheating user identification device, characterized in that: The device comprises: an acquisition module, configured to acquire characteristic data of a plurality of first users, wherein the characteristic data includes geographical locations of the first users when they participate in a network activity initiated by a second user; a determination module, configured to determine whether the second user is a cheating user based on the feature data of the plurality of first users, wherein the cheating user refers to a user who initiates the network activity in a cheating manner; The determining whether the second user is a cheating user based on the feature data of the plurality of first users includes: Determining a geographic dispersion among the plurality of first users based on the geographic locations corresponding to the plurality of first users; comparing the geographic dispersion with a preset dispersion threshold; If the geographic dispersion is greater than the dispersion threshold, the second user is determined to be a cheating user; if the geographic dispersion is less than or equal to the dispersion threshold, the second user is determined not to be a cheating user.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is used to execute a cheating user identification program stored in the memory to implement the cheating user identification method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the cheating user identification method according to any one of claims 1 to 7.

Images