CN114733207B - Game account monitoring analysis early warning management system based on feature analysis - Google Patents
Game account monitoring analysis early warning management system based on feature analysis Download PDFInfo
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- CN114733207B CN114733207B CN202210516167.8A CN202210516167A CN114733207B CN 114733207 B CN114733207 B CN 114733207B CN 202210516167 A CN202210516167 A CN 202210516167A CN 114733207 B CN114733207 B CN 114733207B
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/71—Game security or game management aspects using secure communication between game devices and game servers, e.g. by encrypting game data or authenticating players
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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Abstract
The invention discloses a game account monitoring, analyzing and early warning management system based on feature analysis, which is used for comparing and analyzing the login equipment states of all game accounts in a target game platform by acquiring real-time login equipment information of all game accounts in the target game platform and normal login equipment information in a preset time period, and performing corresponding processing, so that the login safety of the game accounts of the game platform is effectively ensured, and the possibility of theft of the game accounts is reduced; meanwhile, according to the user behavior parameters of each designated game account in the target game platform, the comprehensive safety coefficient of each game account in the target game platform is analyzed, and corresponding early warning processing is carried out according to the comparison analysis result, so that the user behavior parameters after the game account is logged in are monitored in real time, virtual property loss of the user corresponding to the game account is reduced, the game experience of the game user is ensured not to be affected, and the user loss of the game platform is further avoided.
Description
Technical Field
The invention relates to the field of game account monitoring analysis, in particular to a game account monitoring analysis early warning management system based on feature analysis.
Background
The game account security problem is a problem faced by all online games. The lawbreaker acquires the game account number password by different means, such as planting Trojan horse on the login equipment of the game user, or fraudulently acquiring the game account number password in the game by trust of the game user, and after acquiring the account number password of the game user, the lawbreaker logs in the game account number of the user, and steals the game account number resource of the user, thereby obtaining benefits.
At present, in order to ensure the safety of a user game account, each game platform generally only verifies the correctness of the ID and the password of the game account, and whether the login equipment of the game account is normal login equipment is not considered, and the behavior that an illegal person logs in the game account through strange equipment to steal resources exists, so that the login safety of the game account of the game platform cannot be ensured, the possibility of the game account being stolen is further increased, the safety supervision level of the game platform on the user game account is further reduced, and the development of the later game platform is greatly influenced.
Meanwhile, the existing game platform can only conduct safety supervision when the game account logs in, and lacks of conducting real-time supervision on user behavior parameters after the game account logs in, so that the existing game platform has certain limitation on the safety supervision of the game account, the problem that abnormal operation behaviors cannot be timely processed when part of game accounts are stolen exists, virtual property loss of the game account corresponding to users is further increased, and very bad game experience is further brought to game users.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a game account monitoring analysis early warning management system based on feature analysis, which has the following specific technical scheme:
a game account monitoring analysis early warning management system based on feature analysis comprises:
a normally logged-in device information acquisition module: the method comprises the steps of obtaining historical login equipment information of each game account in a target game platform within a preset time period, and comparing to obtain constant login equipment information of each game account in the target game platform within the preset time period;
the game account login equipment state analysis module: the real-time login device information of each game account in the target game platform is obtained, and the login device state of each game account in the target game platform is compared and analyzed;
the game account login equipment state processing module: the method comprises the steps of carrying out corresponding processing according to the login equipment state of each game account in a target game platform, screening each game account successfully logged in the target game platform, and marking each game account as each appointed game account;
game platform database: the method comprises the steps of storing a custom login time range of each game account corresponding to a user and an average online time length of a single login game of the user in a preset time period, simultaneously storing an average operation time length, an average game task operation score, an average game activity participation degree and an average game activity completion degree of each game account corresponding to the single login game of the user in the preset time period, and storing a standard market transaction amount corresponding to each game equipment;
The user behavior parameter acquisition module: the method comprises the steps of acquiring user behavior parameters of each appointed game account in a target game platform, wherein the user behavior parameters comprise user login habit behavior parameters, user operation behavior parameters and user transaction behavior parameters;
user behavior parameter analysis module: the user behavior parameters of each appointed game account in the target game platform are obtained through analysis according to the user behavior parameters of each appointed game account in the target game platform, and the user behavior parameters of each appointed game account in the target game platform are in accordance with the proportionality coefficient;
the game account comprehensive safety coefficient analysis module: and the comprehensive safety coefficient of each game account in the target game platform is analyzed according to the user behavior parameters of each designated game account in the target game platform according to the proportionality coefficient, and is respectively compared with a preset standard safety coefficient threshold value, and corresponding early warning processing is carried out according to the comparison analysis result.
Based on the above embodiment, the specific corresponding acquisition mode in the information acquisition module of the normally logged-in device is:
extracting historical login equipment information of each game account in a preset time period from a background of a target game platform, wherein the historical login equipment information comprises ip addresses of each historical login equipment and MAC addresses of each historical login equipment, and counting the historical login times of each ip address and the historical login times of each MAC address of each game account in the target game platform in the preset time period, if the historical login times of a certain ip address of a certain game account in the target game platform in the preset time period is greater than a preset historical login times threshold value, the ip address is recorded as the ip address of a normally logged-in equipment of the game account in the preset time period, and the ip addresses of the normally logged-in equipment of each game account in the target game platform in the preset time period are counted; and similarly, counting the MAC addresses of all normally-logged-in devices of all the game accounts in the target game platform within a preset time period, and recording the ip addresses of all the normally-logged-in devices of all the game accounts in the target game platform within the preset time period and the MAC addresses of all the normally-logged-in devices as the normally-logged-in device information of the game accounts within the preset time period.
On the basis of the foregoing embodiment, the comparison analysis of the login device state of each game account in the target game platform in the game account login device state analysis module specifically includes:
the method comprises the steps of obtaining real-time login equipment information of each game account in a target game platform, comparing the real-time login equipment information of each game account in the target game platform with normal login equipment information of a corresponding game account in a preset time period, and analyzing to obtain login equipment states of each game account in the target game platform according to preset game account login equipment state analysis rules, wherein the login equipment states comprise a normal state, a state to be verified and an abnormal state.
Based on the above embodiment, the game account login device state processing module performs corresponding processing according to the login device state of each game account in the target game platform, where a specific processing manner is as follows:
according to the login equipment state of each game account in the target game platform, if the login equipment state of a certain game account in the target game platform is in a normal state, allowing a login request of the game account; if the login equipment state of a certain game account in the target game platform is a state to be verified, notifying the corresponding operation user of the game account to perform primary real name verification, and allowing a login request of the game account after the primary real name verification is correct; if the login equipment state of a certain game account in the target game platform is abnormal, notifying a game account corresponding number owner to carry out checking processing, and allowing a login request of the game account after the game account corresponding number owner passes the checking, otherwise, pulling real-time login equipment information of the game account into a platform blacklist.
On the basis of the embodiment, the user login habit behavior parameters comprise user login time and user online time; the user operation behavior parameters comprise user operation time length, game task operation scores, game activity participation degree and game activity completion degree; the user transaction behavior parameters include arming transaction information and medal transaction information, wherein the arming transaction information includes the number of various transaction arming and the total amount of arming transactions, and the medal transaction information includes the medal payout number and the medal payout amount each time.
On the basis of the above embodiment, the analyzing, in the user behavior parameter analyzing module, the user behavior parameters of each designated game account in the target game platform conform to the scaling factor, and the specific analysis content includes:
according to the corresponding user habit logging time range of each game account stored in the game platform database and the average online time length of the user single logging game in the preset time period, extracting the corresponding user habit logging time range of each appointed game account in the preset time period and the average online time length of the user single logging game, comparing the user logging time in the user logging habit behavior parameters of each appointed game account in the target game platform with the corresponding user habit logging time range of each appointed game account in the preset time period to obtain the user logging time coincidence weight factor in the user logging habit behavior parameters of each appointed game account in the target game platform, analyzing the online time length coincidence weight factor of the user in the user logging habit behavior parameters of each appointed game account in the target game platform according to the user online time length coincidence weight factor of each appointed game account in the target game platform, and marking the user logging time coincidence weight factor and the user online time coincidence weight factor in the user habit behavior parameters of each appointed game account in the target game platform as delta respectively i a 1 And delta i a 2 Where i=1, 2,..n, i denotes the number of the i-th designated game account number;
the user login time in the user login habit behavior parameters of each appointed game account in the target game platform is matched with the weight factor delta i a 1 And the online time length of the user accords with a weight factor delta i a 2 Substituting user login habit behavior parameters to accord with proportional coefficient analysis formula xi i ＝λ 1 *δ i a 1 +λ 2 *δ i a 2 Obtaining the user login habit behavior parameters of each appointed game account in the target game platform to accord with the proportionality coefficient xi i Wherein lambda is 1 And lambda (lambda) 2 Respectively denoted as pre-emphasisThe set user login time and the user online time correspond to the correction index.
On the basis of the above embodiment, the analyzing, in the user behavior parameter analyzing module, the user behavior parameters of each designated game account in the target game platform according with the proportionality coefficient, and the specific analysis content further includes:
according to the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each game account stored in the game platform database in a preset time period, the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each designated game account in the preset time period are extracted, and the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each designated game account in the preset time period are respectively marked as
Analyzing the coincidence proportionality coefficient of user operation behavior parameters of each appointed game account in the target game platformWherein->User operation behavior parameters expressed as the ith appointed game account number in the target game platform conform to the proportionality coefficient and sigma 1 、σ 2 、σ 3 Respectively representing the coincidence correction index deltap corresponding to the operation duration, the task operation score and the activity participation completion degree in the preset user operation behaviors Allow for b 1 、Δp Allow for b 3 、Δp Allow for b 4 Respectively expressed as a preset operation time allowable error value, a game activity participation allowable error value and a game activity completion allowable error value of a user single login game, and p i b 1 、p i b 2 、p i b 3 、p i b 4 And e is expressed as a natural constant, wherein the user operation duration, the game task operation score, the game activity participation degree and the game activity completion degree are respectively expressed as user operation duration, game task operation score, game activity participation degree and game activity completion degree in user operation behavior parameters of an ith appointed game account in the target game platform.
On the basis of the above embodiment, the analyzing, in the user behavior parameter analyzing module, the user behavior parameters of each designated game account in the target game platform according with the proportionality coefficient, and the specific analysis content further includes:
extracting standard market transaction amount corresponding to each game equipment stored in a game platform database, screening standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in a target game platform, and marking the standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in the target game platform as r i j J=1, 2,..m, j represents the number of the j-th transaction instrument;
according to the equipment transaction information and the game currency transaction information in the user transaction behavior parameters of each appointed game account in the target game platform, analyzing to obtain the coincidence proportionality coefficient of the user transaction behavior parameters of each appointed game account in the target game platform
Based on the above embodiment, the comprehensive security coefficient analysis module of the game account numbers analyzes the comprehensive security coefficient of each game account number in the target game platform, and the specific analysis mode is as follows:
matching user login habit behavior parameters of each designated game account in a target game platform with a proportionality coefficient xi i The operational behavior parameters of the user conform to the proportionality coefficientAnd the user transaction behavior parameters are in accordance with the proportionality coefficient +.>Substitution formulaObtaining comprehensive safety coefficient omega of each game account in target game platform i Wherein χ is 1 、χ 2 、χ 3 Respectively expressed as safety influence factors corresponding to preset user login habit behaviors, user operation behaviors and user transaction behaviors, and χ 1 +χ 2 +χ 3 ＝1。
Based on the above embodiment, the game account comprehensive security coefficient analysis module performs corresponding early warning processing according to the comparison analysis result, and specifically includes:
and comparing the comprehensive safety coefficient of each game account number in the target game platform with a preset standard safety coefficient threshold value, if the comprehensive safety coefficient of a certain game account number in the target game platform is smaller than the preset standard safety coefficient threshold value, indicating that the game account number in the target game platform is in a theft state, sending a theft early warning prompt to the target game platform, and informing a game account number corresponding number owner to carry out emergency treatment after the target game platform receives the theft early warning prompt sent by the game account number.
The invention has the technical effects that:
according to the game account monitoring, analyzing and early warning management system based on feature analysis, the real-time login equipment information of each game account in the target game platform and the normal login equipment information in the preset time period are obtained, the login equipment states of each game account in the target game platform are compared and analyzed, and corresponding processing is carried out according to the login equipment states of each game account in the target game platform, so that the phenomenon that illegal persons log in the game account through strange equipment to steal resources is avoided, the game account login safety of the game platform is further effectively ensured, the possibility that the game account is stolen is reduced, the safety supervision level of the game platform on the game account of a user is further improved, and the development of the later game platform is promoted to a great extent.
According to the game account monitoring, analyzing and early warning management system based on feature analysis, the user behavior parameters of each appointed game account in the target game platform are obtained, the user behavior parameters of each appointed game account in the target game platform are in accordance with the proportion coefficient, the comprehensive safety coefficient of each game account in the target game platform is analyzed and compared with the preset standard safety coefficient threshold, and corresponding early warning processing is carried out according to the comparison analysis result, so that the user behavior parameters after the game account is logged in are monitored in real time, the limitation of the existing game platform on the game account safety supervision is broken, the problem that the abnormal operation behavior cannot be timely processed after part of game accounts are stolen is avoided to a great extent, virtual property loss of a user corresponding to the game account is further reduced, the game experience of the game user is ensured not to be affected, and the user loss of the game platform is further avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a game account monitoring, analyzing and early warning management system based on feature analysis, which comprises a normally logged-in equipment information acquisition module, a game account logged-in equipment state analysis module, a game account logged-in equipment state processing module, a game platform database, a user behavior parameter acquisition module, a user behavior parameter analysis module and a game account comprehensive safety coefficient analysis module.
The system comprises a constant login device information acquisition module, a game account login device state analysis module, a user behavior parameter acquisition module, a game platform database and a game account comprehensive safety coefficient analysis module, wherein the constant login device information acquisition module is connected with the game account login device state analysis module, the user behavior parameter acquisition module is respectively connected with the game account login device state processing module and the user behavior parameter analysis module, and the user behavior parameter analysis module is respectively connected with the game platform database and the game account comprehensive safety coefficient analysis module.
The constant login equipment information acquisition module is used for acquiring historical login equipment information of each game account in the target game platform within a preset time period, and comparing to obtain constant login equipment information of each game account in the target game platform within the preset time period.
As a preferred solution, the specific corresponding acquisition mode in the information acquisition module of the normally logged-in device is:
extracting historical login equipment information of each game account in a preset time period from a background of a target game platform, wherein the historical login equipment information comprises ip addresses of each historical login equipment and MAC addresses of each historical login equipment, and counting the historical login times of each ip address and the historical login times of each MAC address of each game account in the target game platform in the preset time period, if the historical login times of a certain ip address of a certain game account in the target game platform in the preset time period is greater than a preset historical login times threshold value, the ip address is recorded as the ip address of a normally logged-in equipment of the game account in the preset time period, and the ip addresses of the normally logged-in equipment of each game account in the target game platform in the preset time period are counted; and similarly, counting the MAC addresses of all normally-logged-in devices of all the game accounts in the target game platform within a preset time period, and recording the ip addresses of all the normally-logged-in devices of all the game accounts in the target game platform within the preset time period and the MAC addresses of all the normally-logged-in devices as the normally-logged-in device information of the game accounts within the preset time period.
The game account login device state analysis module is used for acquiring real-time login device information of each game account in the target game platform and comparing and analyzing login device states of each game account in the target game platform.
As a preferred scheme, the game account login device state analysis module compares and analyzes login device states of all game accounts in a target game platform, and specifically comprises:
the method comprises the steps of obtaining real-time login equipment information of each game account in a target game platform, comparing the real-time login equipment information of each game account in the target game platform with normal login equipment information of a corresponding game account in a preset time period, and analyzing to obtain login equipment states of each game account in the target game platform according to preset game account login equipment state analysis rules, wherein the login equipment states comprise a normal state, a state to be verified and an abnormal state.
It should be noted that, the real-time login device information of each game account in the target game platform includes the ip address of the real-time login device and the MAC address of the real-time login device,
further, the preset game account login device state analysis rule is as follows:
If the ip address of the real-time login device and the MAC address of the real-time login device of a certain game account in the target game platform are respectively matched with the ip address of the normal login device and the MAC address of the normal login device of the corresponding game account in a preset time period, the login device state of the game account is marked as a normal state;
if the ip address of a real-time login device or the MAC address of a real-time login device of a game account in a target game platform is matched with the ip address of a normal login device or the MAC address of a normal login device of a corresponding game account in a preset time period, marking the login device state of the game account as a state to be verified;
if the ip address of the real-time login device and the MAC address of the real-time login device of a certain game account in the target game platform are not matched with the ip address of each normally-logged-in device and the MAC address of each normally-logged-in device of the corresponding game account in a preset time period, the login device state of the game account is marked as an abnormal state.
The game account login equipment state processing module is used for carrying out corresponding processing according to the login equipment state of each game account in the target game platform, screening each game account successfully logged in the target game platform, and recording each game account as each appointed game account.
As a preferred solution, the game account login device state processing module performs corresponding processing according to the login device state of each game account in the target game platform, where a specific processing manner is as follows:
according to the login equipment state of each game account in the target game platform, if the login equipment state of a certain game account in the target game platform is in a normal state, allowing a login request of the game account; if the login equipment state of a certain game account in the target game platform is a state to be verified, notifying the corresponding operation user of the game account to perform primary real name verification, and allowing a login request of the game account after the primary real name verification is correct; if the login equipment state of a certain game account in the target game platform is abnormal, notifying a game account corresponding number owner to carry out checking processing, and allowing a login request of the game account after the game account corresponding number owner passes the checking, otherwise, pulling real-time login equipment information of the game account into a platform blacklist.
The game platform database is used for storing the average online time length of each game account corresponding to the user habit login time range and the user single login game in a preset time period, simultaneously storing the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of each game account corresponding to the user single login game in the preset time period, and storing the standard market transaction amount corresponding to each game equipment.
In the embodiment, the real-time login equipment information of each game account in the target game platform and the normal login equipment information in the preset time period are acquired, the login equipment state of each game account in the target game platform is compared and analyzed, and corresponding processing is carried out according to the login equipment state of each game account in the target game platform, so that the phenomenon that illegal molecules log in the game account through strange equipment to steal resources is avoided, the login safety of the game account of the game platform is further effectively ensured, the possibility of the game account being stolen is reduced, the safety supervision level of the game platform on the game account of a user is further improved, and the development of the later game platform is promoted to a great extent.
The user behavior parameter acquisition module is used for: the method comprises the steps of acquiring user behavior parameters of each appointed game account in a target game platform, wherein the user behavior parameters comprise user login habit behavior parameters, user operation behavior parameters and user transaction behavior parameters.
As a preferred scheme, the user login habit behavior parameters comprise user login time and user online time; the user operation behavior parameters comprise user operation time length, game task operation scores, game activity participation degree and game activity completion degree; the user transaction behavior parameters include arming transaction information and medal transaction information, wherein the arming transaction information includes the number of various transaction arming and the total amount of arming transactions, and the medal transaction information includes the medal payout number and the medal payout amount each time.
The user behavior parameter analysis module is used for analyzing and obtaining the user behavior parameters of each appointed game account in the target game platform according to the user behavior parameters of each appointed game account in the target game platform.
As a preferred solution, the analyzing, in the user behavior parameter analyzing module, the user behavior parameters of each designated game account in the target game platform conform to the scaling factor, and the specific analysis content includes:
according to the user habit login time range and the average online time length of the user single login game corresponding to each game account stored in the game platform database in the preset time period, extracting the user habit login time range and the average online time length of the user single login game corresponding to each appointed game account in the preset time period, and putting each appointed game account in the target game platformComparing the user login time in the user login habit behavior parameters of each designated game account with the corresponding user habit login time range of the designated game account in a preset time period to obtain the user login time coincidence weight factor in the user login habit behavior parameters of each designated game account in the target game platform, analyzing to obtain the user online time coincidence weight factor in the user login habit behavior parameters of each designated game account in the target game platform according to the user online time length in the user login habit behavior parameters of each designated game account in the target game platform, and marking the user login time coincidence weight factor and the user online time coincidence weight factor in the user login habit behavior parameters of each designated game account in the target game platform as delta respectively i a 1 And delta i a 2 Where i=1, 2,..n, i denotes the number of the i-th designated game account number;
the user login time in the user login habit behavior parameters of each appointed game account in the target game platform is matched with the weight factor delta i a 1 And the online time length of the user accords with a weight factor delta i a 2 Substituting user login habit behavior parameters to accord with proportional coefficient analysis formula xi i ＝λ 1 *δ i a 1 +λ 2 *δ i a 2 Obtaining the user login habit behavior parameters of each appointed game account in the target game platform to accord with the proportionality coefficient xi i Wherein lambda is 1 And lambda (lambda) 2 And respectively representing the preset user login time and the corresponding coincidence correction index of the user on-line time.
It should be noted that, the user login time coincidence weight factor comparison obtaining mode in the user login habit behavior parameters of each designated game account in the target game platform is as follows:
if the user login time in the user login habit behavior parameters of a specific game account in the target game platform is within the range of the corresponding user habit login time of the specific game account in the preset time period, the user login time in the user login habit behavior parameters of the specific game account in the target game platform accords with the weight factor delta', otherwise, The user login time coincidence weight factor delta 'in the user login habit behavior parameters of the appointed game account numbers in the target game platform is counted, and the user login time coincidence weight factor delta' in the user login habit behavior parameters of all the appointed game account numbers in the target game platform is counted i a 1 Wherein delta i a 1 =δ' or δ ".
It should be noted that, the analysis mode formula of the user online time length coincidence weight factor in the user login habit behavior parameters of each designated game account in the target game platform is as followsWherein mu is expressed as a preset on-line time length compensation index, deltat Allow for Representing a preset allowable error value, t, of the online time length of the user i User online time length in user login habit behavior parameters expressed as ith appointed game account number in target game platform,/user online time length of user login habit behavior parameters expressed as ith appointed game account number in target game platform>And representing the average online time length of the ith appointed game account number corresponding to the single login game of the user in a preset time period.
As a preferred solution, the analyzing module for analyzing the user behavior parameters of each designated game account in the target game platform according to the proportionality coefficient, and the specific analysis content further includes:
according to the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each game account stored in the game platform database in a preset time period, the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each designated game account in the preset time period are extracted, and the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of the single-login game of the corresponding user of each designated game account in the preset time period are respectively marked Is marked as
Analyzing the coincidence proportionality coefficient of user operation behavior parameters of each appointed game account in the target game platformWherein->User operation behavior parameters expressed as the ith appointed game account number in the target game platform conform to the proportionality coefficient and sigma 1 、σ 2 、σ 3 Respectively representing the coincidence correction index deltap corresponding to the operation duration, the task operation score and the activity participation completion degree in the preset user operation behaviors Allow for b 1 、Δp Allow for b 3 、Δp Allow for b 4 Respectively expressed as a preset operation time allowable error value, a game activity participation allowable error value and a game activity completion allowable error value of a user single login game, and p i b 1 、p i b 2 、p i b 3 、p i b 4 And e is expressed as a natural constant, wherein the user operation duration, the game task operation score, the game activity participation degree and the game activity completion degree are respectively expressed as user operation duration, game task operation score, game activity participation degree and game activity completion degree in user operation behavior parameters of an ith appointed game account in the target game platform.
As a preferred solution, the analyzing module for analyzing the user behavior parameters of each designated game account in the target game platform according to the proportionality coefficient, and the specific analysis content further includes:
extracting standard market transaction amount corresponding to each game equipment stored in a game platform database, screening standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in a target game platform, and marking the standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in the target game platform as r i j J=1, 2,..m, j represents the number of the j-th transaction instrument;
according to the equipment transaction information and the game currency transaction information in the user transaction behavior parameters of each appointed game account in the target game platform, analyzing to obtain the coincidence proportionality coefficient of the user transaction behavior parameters of each appointed game account in the target game platform
It should be noted that, the user transaction behavior parameters of each designated game account in the above target game platform conform to the analysis formula of the proportionality coefficient as followsη 1 And eta 2 Respectively expressed as a coincidence correction index, R 'corresponding to preset equipment transaction information and game currency transaction information' i The transaction total amount, x, is loaded into the user transaction behavior parameters, which are expressed as the ith designated game account number in the target game platform i j Respectively expressed as the number of j transaction equipments in the user transaction behavior parameters of the i-th appointed game account number in the target game platform, delta R Error of Expressed as a preset gaming equipment transaction allowable error amount, y i Pre-preparation A single-pass payoff amount threshold value, y, preset for the ith designated game account number i f The amount of the expenditure of the f-th game currency in the user transaction behavior parameters expressed as the i-th designated game account number in the target game platform, f=1, 2.. i The number of payouts of the game currency in the user transaction behavior parameters of the ith appointed game account number in the target game platform is represented.
The game account comprehensive safety coefficient analysis module is used for analyzing the comprehensive safety coefficient of each game account in the target game platform according to the proportion coefficient of the user behavior parameters of each designated game account in the target game platform, comparing the comprehensive safety coefficient with a preset standard safety coefficient threshold value respectively, and carrying out corresponding early warning processing according to the comparison analysis result.
As a preferred scheme, the comprehensive safety coefficient analysis module of the game account numbers analyzes the comprehensive safety coefficient of each game account number in the target game platform in the following specific analysis modes:
matching user login habit behavior parameters of each designated game account in a target game platform with a proportionality coefficient xi i The operational behavior parameters of the user conform to the proportionality coefficientAnd the user transaction behavior parameters are in accordance with the proportionality coefficient +.>Substitution formulaObtaining comprehensive safety coefficient omega of each game account in target game platform i Wherein χ is 1 、χ 2 、χ 3 Respectively expressed as safety influence factors corresponding to preset user login habit behaviors, user operation behaviors and user transaction behaviors, and χ 1 +χ 2 +χ 3 ＝1。
As a preferred solution, the game account comprehensive security coefficient analysis module performs corresponding early warning processing according to a comparison analysis result, and specifically includes:
And comparing the comprehensive safety coefficient of each game account number in the target game platform with a preset standard safety coefficient threshold value, if the comprehensive safety coefficient of a certain game account number in the target game platform is smaller than the preset standard safety coefficient threshold value, indicating that the game account number in the target game platform is in a theft state, sending a theft early warning prompt to the target game platform, and informing a game account number corresponding number owner to carry out emergency treatment after the target game platform receives the theft early warning prompt sent by the game account number.
In this embodiment, the user behavior parameters of each designated game account in the target game platform are obtained by obtaining the user behavior parameters of each designated game account in the target game platform, the user behavior parameters of each designated game account in the target game platform are in accordance with the proportionality coefficient, the comprehensive safety coefficient of each game account in the target game platform is analyzed and compared with the preset standard safety coefficient threshold, and corresponding early warning processing is performed according to the comparison analysis result, so that the user behavior parameters after the game account is logged in are monitored in real time, the limitation of the existing game platform on the safety supervision of the game account is broken, the problem that abnormal operation behaviors cannot be processed in time after part of game accounts are stolen is avoided to a great extent, further virtual property loss of users corresponding to the game account is reduced, the game experience of game users is ensured not to be affected, and the user loss of the game platform is further avoided.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (8)
1. A game account monitoring, analyzing and early warning management system based on feature analysis is characterized by comprising:
a normally logged-in device information acquisition module: the method comprises the steps of obtaining historical login equipment information of each game account in a target game platform within a preset time period, and comparing to obtain constant login equipment information of each game account in the target game platform within the preset time period;
the game account login equipment state analysis module: the real-time login device information of each game account in the target game platform is obtained, and the login device state of each game account in the target game platform is compared and analyzed;
the game account login equipment state processing module: the method comprises the steps of carrying out corresponding processing according to the login equipment state of each game account in a target game platform, screening each game account successfully logged in the target game platform, and marking each game account as each appointed game account;
Game platform database: the method comprises the steps of storing a custom login time range of each game account corresponding to a user and an average online time length of a single login game of the user in a preset time period, simultaneously storing an average operation time length, an average game task operation score, an average game activity participation degree and an average game activity completion degree of each game account corresponding to the single login game of the user in the preset time period, and storing a standard market transaction amount corresponding to each game equipment;
the user behavior parameter acquisition module: the method comprises the steps of acquiring user behavior parameters of each appointed game account in a target game platform, wherein the user behavior parameters comprise user login habit behavior parameters, user operation behavior parameters and user transaction behavior parameters;
user behavior parameter analysis module: the user behavior parameters of each appointed game account in the target game platform are obtained through analysis according to the user behavior parameters of each appointed game account in the target game platform, and the user behavior parameters of each appointed game account in the target game platform are in accordance with the proportionality coefficient;
the game account comprehensive safety coefficient analysis module: the system comprises a target game platform, a target game system and a user behavior parameter analysis module, wherein the target game platform is used for analyzing the comprehensive safety coefficient of each game account according to the user behavior parameters of each designated game account in the target game platform according to the proportionality coefficient, comparing the comprehensive safety coefficient with a preset standard safety coefficient threshold value respectively, and carrying out corresponding early warning processing according to a comparison analysis result;
The user login habit behavior parameters comprise user login time and user online time; the user operation behavior parameters comprise user operation time length, game task operation scores, game activity participation degree and game activity completion degree; the user transaction behavior parameters comprise arming transaction information and game currency transaction information, wherein the arming transaction information comprises the quantity of various transaction arming and the total amount of arming transaction, and the game currency transaction information comprises the number of game currency payout times and the amount of each game currency payout;
the user behavior parameter analysis module analyzes that the user behavior parameters of each appointed game account in the target game platform accord with the proportionality coefficient, and the specific analysis content further comprises:
according to the average operation duration of each game account stored in the game platform database corresponding to the single-login game of the user in the preset time period, and the average gameThe method comprises the steps of extracting the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of each appointed game account number corresponding to a user single login game in a preset time period, and marking the average operation time length, the average game task operation score, the average game activity participation degree and the average game activity completion degree of each appointed game account number corresponding to the user single login game in the preset time period as follows
Analyzing the coincidence proportionality coefficient of user operation behavior parameters of each appointed game account in the target game platform
Wherein the method comprises the steps ofUser operation behavior parameters expressed as the ith appointed game account number in the target game platform conform to the proportionality coefficient and sigma 1 、σ 2 、σ 3 Respectively representing the coincidence correction index deltap corresponding to the operation duration, the task operation score and the activity participation completion degree in the preset user operation behaviors Allow for b 1 、Δp Allow for b 3 、Δp Allow for b 4 Respectively expressed as a preset operation time allowable error value, a game activity participation allowable error value and a game activity completion allowable error value of a user single login game, and p i b 1 、p i b 2 、p i b 3 、p i b 4 And e is expressed as a natural constant, wherein the user operation duration, the game task operation score, the game activity participation degree and the game activity completion degree are respectively expressed as user operation duration, game task operation score, game activity participation degree and game activity completion degree in user operation behavior parameters of an ith appointed game account in the target game platform.
2. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein the game account monitoring analysis early warning management system based on feature analysis is characterized in that: the specific corresponding acquisition mode in the information acquisition module of the normally logged-in equipment is as follows:
extracting historical login equipment information of each game account in a preset time period from a background of a target game platform, wherein the historical login equipment information comprises ip addresses of each historical login equipment and MAC addresses of each historical login equipment, and counting the historical login times of each ip address and the historical login times of each MAC address of each game account in the target game platform in the preset time period, if the historical login times of a certain ip address of a certain game account in the target game platform in the preset time period is greater than a preset historical login times threshold value, the ip address is recorded as the ip address of a normally logged-in equipment of the game account in the preset time period, and the ip addresses of the normally logged-in equipment of each game account in the target game platform in the preset time period are counted; and similarly, counting the MAC addresses of all normally-logged-in devices of all the game accounts in the target game platform within a preset time period, and recording the ip addresses of all the normally-logged-in devices of all the game accounts in the target game platform within the preset time period and the MAC addresses of all the normally-logged-in devices as the normally-logged-in device information of the game accounts within the preset time period.
3. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the game account login device state analysis module compares and analyzes login device states of all game accounts in a target game platform, and specifically comprises the following steps:
the method comprises the steps of obtaining real-time login equipment information of each game account in a target game platform, comparing the real-time login equipment information of each game account in the target game platform with normal login equipment information of a corresponding game account in a preset time period, and analyzing to obtain login equipment states of each game account in the target game platform according to preset game account login equipment state analysis rules, wherein the login equipment states comprise a normal state, a state to be verified and an abnormal state.
4. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the game account login device state processing module carries out corresponding processing according to the login device state of each game account in the target game platform, and the specific processing mode is as follows:
according to the login equipment state of each game account in the target game platform, if the login equipment state of a certain game account in the target game platform is in a normal state, allowing a login request of the game account; if the login equipment state of a certain game account in the target game platform is a state to be verified, notifying the corresponding operation user of the game account to perform primary real name verification, and allowing a login request of the game account after the primary real name verification is correct; if the login equipment state of a certain game account in the target game platform is abnormal, notifying a game account corresponding number owner to carry out checking processing, and allowing a login request of the game account after the game account corresponding number owner passes the checking, otherwise, pulling real-time login equipment information of the game account into a platform blacklist.
5. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the user behavior parameter analysis module analyzes that the user behavior parameters of each appointed game account in the target game platform accord with the proportionality coefficient, and the specific analysis content comprises:
according to the user habit login time range and the average online time length of the single-time login game of each game account stored in the game platform database in the preset time period, extracting the user habit login time range and the average online time length of the single-time login game of each designated game account in the preset time period, comparing the user login time in the user login habit behavior parameters of each designated game account in the target game platform with the user habit login time range of each designated game account in the preset time period, and obtaining the user login habit of each designated game account in the target game platformAccording to the user login time coincidence weight factor in the parameters and according to the user online time length in the user login habit behavior parameters of each appointed game account in the target game platform, analyzing to obtain the user online time length coincidence weight factor in the user login habit behavior parameters of each appointed game account in the target game platform, and respectively marking the user login time coincidence weight factor and the user online time length coincidence weight factor in the user login habit behavior parameters of each appointed game account in the target game platform as delta i a 1 And delta i a 2 Where i=1, 2,..n, i denotes the number of the i-th designated game account number;
the user login time in the user login habit behavior parameters of each appointed game account in the target game platform is matched with the weight factor delta i a 1 And the online time length of the user accords with a weight factor delta i a 2 Substituting user login habit behavior parameters to accord with proportional coefficient analysis formula xi i ＝λ 1 *δ i a 1 +λ 2 *δ i a 2 Obtaining the user login habit behavior parameters of each appointed game account in the target game platform to accord with the proportionality coefficient xi i Wherein lambda is 1 And lambda (lambda) 2 And respectively representing the preset user login time and the corresponding coincidence correction index of the user on-line time.
6. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the user behavior parameter analysis module analyzes that the user behavior parameters of each appointed game account in the target game platform accord with the proportionality coefficient, and the specific analysis content further comprises:
extracting standard market transaction amount corresponding to each game equipment stored in a game platform database, screening standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in a target game platform, and marking the standard market transaction amount corresponding to each transaction equipment in user transaction behavior parameters of each designated game account in the target game platform as r i j ，j＝1,2,...,m，j represents the number of the j-th transaction equipment;
according to the equipment transaction information and the game currency transaction information in the user transaction behavior parameters of each appointed game account in the target game platform, analyzing to obtain the coincidence proportionality coefficient of the user transaction behavior parameters of each appointed game account in the target game platform
7. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the comprehensive safety coefficient analysis module of the game account numbers analyzes the comprehensive safety coefficient of each game account number in the target game platform in the following specific analysis modes:
matching user login habit behavior parameters of each designated game account in a target game platform with a proportionality coefficient xi i The operational behavior parameters of the user conform to the proportionality coefficientAnd the user transaction behavior parameters are in accordance with the proportionality coefficient +.>Substitution formulaObtaining comprehensive safety coefficient omega of each game account in target game platform i Wherein χ is 1 、χ 2 、χ 3 Respectively expressed as safety influence factors corresponding to preset user login habit behaviors, user operation behaviors and user transaction behaviors, and χ 1 +χ 2 +χ 3 ＝1。
8. The game account monitoring analysis early warning management system based on feature analysis according to claim 1, wherein: the game account comprehensive safety coefficient analysis module performs corresponding early warning processing according to the comparison analysis result, and specifically comprises the following steps:
And comparing the comprehensive safety coefficient of each game account number in the target game platform with a preset standard safety coefficient threshold value, if the comprehensive safety coefficient of a certain game account number in the target game platform is smaller than the preset standard safety coefficient threshold value, indicating that the game account number in the target game platform is in a theft state, sending a theft early warning prompt to the target game platform, and informing a game account number corresponding number owner to carry out emergency treatment after the target game platform receives the theft early warning prompt sent by the game account number.
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