CN112737851A - Internet anti-cheating identification method and platform - Google Patents
Internet anti-cheating identification method and platform Download PDFInfo
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- CN112737851A CN112737851A CN202011617759.6A CN202011617759A CN112737851A CN 112737851 A CN112737851 A CN 112737851A CN 202011617759 A CN202011617759 A CN 202011617759A CN 112737851 A CN112737851 A CN 112737851A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/0695—Management of faults, events, alarms or notifications the faulty arrangement being the maintenance, administration or management system
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L47/29—Flow control; Congestion control using a combination of thresholds
Abstract
The invention discloses an internet anti-cheating identification method and a platform, which relate to the technical field of internet information, and the method comprises the following steps: s1: accessing data of each service line, monitoring each data fluctuation, presetting a threshold, and triggering an alarm mechanism when the magnitude of a data fluctuation request exceeds the threshold; s2: identifying a cheating request from a plurality of dimensions; s3: and returning error codes and asynchronous call backs in real time. According to the invention, risk requests are identified from multiple dimensions, the risk of cheating on a control line is reduced, healthy and fair experience of users is ensured, and good internet ecology is maintained.
Description
Technical Field
The invention relates to the technical field of internet information, in particular to an internet anti-cheating identification method and an internet anti-cheating identification platform.
Background
At present, the internet has more cheating attacks, including false registration, marketing activity 'wool', attention/fan brushing, group cheating and the like, and a great amount of cheating behaviors seriously influence the use experience of users and cause user loss. While the associated company or organization may suffer a significant amount of capital and loss of reputation due to the detriment of the user experience. Meanwhile, with the continuous promotion of cheating income, the technical means of cheaters are continuously updated, for example:
1. the black industrial chain is gradually perfected, the upstream part is responsible for collecting and providing various network black product resources, the midstream part is responsible for developing and customizing a large number of black product tools, the downstream part is responsible for trading and reproducing the black product activities, the cost of cheating attack is reduced, and the attack magnitude is very large
2. The technical level of promoting black production is rapidly improved, such as cracking/forging interface protocol, simulating user clicking/operating, completing operations of inputting, clicking, moving and the like of interface input control by triggering mouse and keyboard lamp, and on-line cheating fighting violently
3. The service line has certain potential safety hazards, for example, the black products are sensitive to the service, and security holes are easy to discover. Meanwhile, the safety capability of part of new services is weak, and a wind control mechanism is often ignored and is easily utilized by black products.
However, at present, the main cheating identification mode in the industry is single, and the large-scale cheating magnitude is intercepted mainly through basic means such as frequency control before submitting behaviors, and the online omission is further reduced through a manual review mode subsequently. However, the scheme has a poor effect, as exemplified by a certain content community in China, the daily cheating request amount reaches tens of millions, the method using single wind control countermeasure has a poor effect, is easy to be bypassed by cheating users, can cause certain accidental injuries to normal users, has a poor comprehensive effect (the cheating and leakage rate is greater than 10%), needs hundreds of auditing teams to support, and has very serious resource loss.
Disclosure of Invention
In order to solve at least or partially solve the problems, the anti-cheating identification method for the internet is provided, risk requests are identified from multiple dimensions, cheating risks on a control line are reduced, a user is guaranteed to obtain healthy and fair experience, and good internet ecology is maintained.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention discloses an internet anti-cheating identification method, which comprises the following steps:
s1: accessing data of each service line, monitoring each data fluctuation, presetting a threshold, and triggering an alarm mechanism when the magnitude of a data fluctuation request exceeds the threshold;
s2: identifying a cheating request from a plurality of dimensions;
s3: and returning error codes and asynchronous call backs in real time.
As a preferred technical solution of the present invention, in the step S1, a visual data viewing manner is supported during data monitoring, and data statistics is refreshed in real time.
As a preferred technical solution of the present invention, in the step S2, the multiple dimensions include a content dimension, a behavior dimension, and an account dimension.
As a preferred technical scheme of the invention, the content dimension establishes a content identification matrix according to a historical cheating content library, calculates the similarity between the newly added content and the content identification matrix, and determines an identification result according to a similarity threshold.
As a preferred technical scheme of the invention, the content identification matrix comprises an image model, a sensitive word, a text model and a repeated string model.
As a preferred technical scheme of the invention, the behavior dimension clusters the behavior frequency of the current information by adopting a clustering algorithm according to a black sample library, determines the category of the current information behavior, performs abnormal parameter validation according to the category, and determines the identification result according to the validation result.
As a preferred technical scheme of the invention, the account dimensionality is determined by linkage identification in a user association mode.
The invention discloses an internet anti-cheating identification platform which comprises a risk monitoring module, an abnormal early warning module, a data access module, a risk identification module and a risk processing module, wherein the data access module accesses data of each service line and sends the data to the risk monitoring module, the risk monitoring module monitors each data fluctuation and sends the monitored data condition to the abnormal early warning module, the abnormal early warning module is preset with a threshold value, when the magnitude of a data fluctuation request exceeds the threshold value, an alarm mechanism is triggered to activate the risk identification module, the risk identification module comprises a content dimension module, an action dimension module and an account dimension module, the content dimension module establishes a content identification matrix according to a historical cheating content library, calculates the similarity between newly added content and the content identification matrix, determines an identification result according to the similarity threshold value, and the action dimension module carries out clustering on the action frequency of current information according to a black sample library by adopting a clustering algorithm The system comprises a category determination module, an account number dimension module, a risk identification module and a risk processing module, wherein the category determination module determines the category of current information behaviors, performs abnormal parameter validation according to the category, determines an identification result according to the validation result, the account number dimension module determines the identification result by linkage identification in a user association mode, the risk identification module starts the risk processing module according to the risk identification result, and the risk processing module performs data interception by adopting a mechanism of returning error codes in real time and asynchronous call-back.
Compared with the prior art, the invention has the following beneficial effects:
1: the cheating recognition application effect is good
Through a multi-dimensional identification mode, including before, after, content, behavior, account number and the like, the risk request can be better covered, and the overall online effect is improved. Meanwhile, the proper treatment means is used, so that the experience of accidental injury of the user is ensured
2: wide range of cheating identification coverage
Through a perfect service docking process, the function points of each service are covered, no leak is ensured, the function points cannot be easily caught by black-product organizations, and the overall coverage capability is improved
3: fast iteration of cheating recognition capability
Through an automatic cheating request discovery mechanism and a strategy platform capability, online problems can be discovered and processed more quickly, and loss can be stopped in time
4: reduce the manpower input of audit
The audit budget investment can be reduced by more than 80% through estimation, and the enterprise pressure is relieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of the system architecture of the present invention;
in the figure: 1. a risk monitoring module; 2. an anomaly early warning module; 3. a data access module; 4. a risk identification module; 5. a risk processing module; 6. a content dimension module; 7. a behavior dimension module; 8. and an account dimension module.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation. Wherein like reference numerals refer to like parts throughout.
In addition, if a detailed description of the known art is not necessary to show the features of the present invention, it is omitted.
Example 1
The invention provides an internet anti-cheating identification method, which comprises the following steps:
s1: accessing data of each service line, monitoring each data fluctuation, presetting a threshold, and triggering an alarm mechanism when the magnitude of a data fluctuation request exceeds the threshold;
s2: identifying a cheating request from a plurality of dimensions;
s3: and returning error codes and asynchronous call backs in real time.
In step S1, a visual data viewing mode is supported during data monitoring, and data statistics are refreshed in real time.
The plurality of dimensions in step S2 includes a content dimension, a behavior dimension, and an account dimension.
And establishing a content identification matrix according to the historical cheating content library by the content dimension, calculating the similarity between the newly added content and the content identification matrix, and determining an identification result according to a similarity threshold value.
The content identification matrix comprises an image model, a sensitive word, a text model and a repeated string model.
And the behavior dimension carries out clustering on the behavior frequency of the current information according to a black sample library by adopting a clustering algorithm, determines the category of the current information behavior, carries out abnormal parameter validation according to the category and determines an identification result according to a validation result.
And determining an identification result by linkage identification through the account dimension in a user association mode.
1) The main idea comprises the steps of constructing an anti-cheating strategy system and automatically identifying cheating requests
a) And (3) discovering the strategy: rapidly finding newly added risks on line through on-line mechanism and stopping loss in time
b) Identifying a strategy: identifying cheating requests from multiple dimensions through an online mechanism, ensuring accuracy and recall
c) The disposal strategy is as follows: by using a more appropriate disposal mode, on the premise of guaranteeing normal user experience, cheating requests/cheating users are better attacked, and overall risks on the line are controlled.
2) Establishing a wind control strategy platform to improve the on-line real-time confrontation efficiency
a) Supporting data analysis: data export analysis supporting all dimensions and intelligent system auxiliary analysis mechanism established
b) Support for policy visualization configuration capabilities: reduce the strategy configuration cost, respond to the on-line problem in time and improve the overall on-line confrontation effect
c) Support multiple environments: the method comprises a plurality of environments such as off-line test, gray level test, small flow test and the like, improves data analysis safety, and ensures on-line effect.
3) A service docking mechanism is established, and the potential safety hazard of each service side is reduced
a) The cooperation mechanism: establishing perfect service butt joint flow, ensuring service safety and meeting the requirements of on-line
b) Existing business risk investigation: important service scenes of the service line are checked, and the wind control capability is timely supplemented for the services with security holes
Outputting the general wind control capacity: and establishing a general service docking scheme to ensure that the new service line quickly has an anti-cheating mechanism with certain capacity.
Anti-cheating policy system
And (3) finding a problem strategy:
adding an automatic risk monitoring and early warning mechanism, performing statistical monitoring and warning aiming at abnormal fluctuation on each risk event, and quickly finding problems
The scheme is as follows:
and accessing data of each service line, monitoring and early warning aiming at each data fluctuation, triggering an alarm mechanism when the magnitude of the request abnormally fluctuates, supporting a visual data viewing mode and refreshing data statistics in real time.
Risk identification strategy:
the identification mechanism comprises:
intercepting in advance: scenarios where some non-important submission actions, e.g. browsing, take place before a risk event occurs
In-service confrontation: when a risk event occurs, such as order submission, logistics period and the like, risk behaviors can be stopped in time, and loss can be recovered
Backtracking: after the risk event occurs, such as the completion of an order, the successful post publishing and other scenes, further mining and backtracking are performed, and omission is reduced.
Identifying the dimension:
content dimension:
the problems encountered are:
text direction: the interference in the cheating text is more, and the sensitive word/model strategy is usually bypassed by using cheating content variation and random interference content.
The picture direction is as follows: the cheating picture has quick variation, the cheating user utilizes a large amount of background elements and the identification capability of an interference disturbance model, and the identification difficulty is high
The method comprises the steps of establishing a content identification matrix, and matching with a high-risk content automatic cleaning mechanism to attack cheating of known types and unknown types.
Behavior dimension:
the problems encountered are: aggregate large-scale cheating behaviors in a machine mode to cause a large number of abnormal attacks
The solution is as follows: the behavior cheating is attacked through a frequency control rule, a risk clustering strategy, a sample black library, abnormal parameter verification and a real-time countermeasure model mixing mechanism.
Account number dimension:
the problems encountered are: the magnitude of the cheating account is large, and the anti-cheating strategy is easy to cause omission.
The solution is as follows: and (4) linking, identifying and processing the cheating users in a user association mode.
Risk handling policy:
verification type treatment: for example, multiple processing modes such as verification codes, short message verification, voice verification, face verification and the like are used for identifying whether the current user has safety or cheating risks. The method has the advantages that the user experience is good, but the method is easy to be broken by cheating users, and the method is suitable for scenes with low accuracy.
Penalty type treatment: handling, such as deleting, blocking, etc., handles requests for risk discovery directly. The method has the advantages of good treatment effect, poor experience when the user is accidentally injured, and suitability for scenes with high accuracy.
The invention relates to an internet anti-cheating identification platform based on the method, which comprises a risk monitoring module 1, an abnormal early warning module 2, a data access module 3, a risk identification module 4 and a risk processing module 5, wherein the data access module 3 accesses data of each service line and sends the data to the risk monitoring module 1, the risk monitoring module 1 monitors each data fluctuation and sends the monitored data condition to the abnormal early warning module 2, the abnormal early warning module 2 presets a threshold value, when the magnitude of a data fluctuation request exceeds the threshold value, an alarm mechanism is triggered to activate the risk identification module 4, the risk identification module 4 comprises a content dimension module 6, a behavior dimension module 7 and an account dimension module 8, the content dimension module 6 establishes a content identification matrix according to a historical cheating content library, calculates the similarity between newly added content and the content identification matrix, and determines an identification result according to the similarity threshold value, the behavior dimension module 7 clusters the behavior frequency of the current information according to a black sample library by adopting a clustering algorithm, determines the category of the behavior of the current information, performs abnormal parameter validation according to the category, determines an identification result according to the validation result, the account dimension module 8 determines the identification result by adopting linkage identification through a user association mode, the risk identification module 4 starts the risk processing module 5 according to the risk identification result, and the risk processing module 5 adopts a mechanism of returning error codes in real time and asynchronously calling back to intercept data.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An internet anti-cheating identification method is characterized by comprising the following steps:
s1: accessing data of each service line, monitoring each data fluctuation, presetting a threshold, and triggering an alarm mechanism when the magnitude of a data fluctuation request exceeds the threshold;
s2: identifying a cheating request from a plurality of dimensions;
s3: and returning error codes and asynchronous call backs in real time.
2. The internet anti-cheating recognition method of claim 1, wherein in step S1, a visual data viewing manner is supported during data monitoring, and data statistics are refreshed in real time.
3. The internet anti-cheating recognition method of claim 1, wherein the plurality of dimensions in step S2 include a content dimension, a behavior dimension, and an account dimension.
4. The internet anti-cheating recognition method of claim 3, wherein the content dimension establishes a content recognition matrix according to a historical cheating content library, calculates similarity between newly added content and the content recognition matrix, and determines a recognition result according to a similarity threshold.
5. The internet anti-cheating recognition method of claim 4, wherein the content recognition matrix comprises a picture model, a sensitive word, a text model, and a repeat string model.
6. The internet anti-cheating identification method according to claim 3, wherein the behavior dimension clusters the behavior frequency of the current information according to a black sample library by using a clustering algorithm to determine the category of the current information behavior, performs abnormal parameter validation according to the category, and determines the identification result according to the validation result.
7. The internet anti-cheating recognition method of claim 3, wherein the account dimension is determined by linkage recognition through a user association mode.
8. An internet anti-cheating identification platform is characterized by comprising a risk monitoring module (1), an abnormal early warning module (2), a data access module (3), a risk identification module (4) and a risk processing module (5), wherein the data access module (3) accesses data of each service line and sends the data to the risk monitoring module (1), the risk monitoring module (1) monitors each data fluctuation and sends the monitored data condition to the abnormal early warning module (2), the abnormal early warning module (2) presets a threshold value, when the data fluctuation request magnitude exceeds the threshold value, an alarm mechanism is triggered to activate the risk identification module (4), the risk identification module (4) comprises a content dimension module (6), a behavior dimension module (7) and an account dimension module (8), and the content dimension module (6) establishes a content identification matrix according to a historical cheating content library, calculating the similarity between the newly added content and a content identification matrix, determining an identification result according to a similarity threshold, clustering the behavior frequency of the current information according to a black sample library by using a clustering algorithm by using a behavior dimension module (7), determining the category of the current information behavior, performing abnormal parameter validation according to the category, determining the identification result according to the validation result, determining the identification result by using linkage identification through a user association mode by using an account dimension module (8), starting a risk processing module (5) according to the risk identification result by using a risk identification module (4), and intercepting data by using a mechanism of returning error codes in real time and asynchronous call-back by using the risk processing module (5).
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| CN117032052A (en) * | 2023-10-07 | 2023-11-10 | 华能信息技术有限公司 | Security control method and system based on dynamic event |
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Application publication date: 20210430 |