Player Clustering System for Real-Time Cheater Isolation
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Solution Overview
Problem
Existing anti-cheat software is inadequate in detecting unknown or difficult-to-detect cheating methods in multiplayer games, leading to a need for immediate isolation of cheaters and alerting game administrators.
Innovation Solution
An information processing system that generates feature information for each player using weighted values based on operation history, with first weight control increasing values for peculiar tendencies and second weight control adjusting values over time, allowing for real-time clustering and isolation of cheaters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing anti-cheat software is used, then known cheating methods can be detected, but unknown or difficult-to-detect cheating methods cannot be identified
Solution Approach 1:
The system dynamically adjusts detection thresholds and re-clusters player groups based on changing operation patterns. As new cheating methods emerge and spread, the system continuously updates its understanding of normal versus abnormal behavior, allowing it to adapt to unknown cheating techniques rather than relying on static detection rules
Solution Approach 2:
The system implements feedback loops where detection results are fed back into the clustering algorithm to refine player groupings. When cheaters are detected and isolated, their operation patterns are analyzed to update the understanding of abnormal behavior, improving future detection of both known and unknown cheating methods
2Productivity
If traditional cheating detection methods are used, then detection speed is limited, but immediate isolation of cheaters cannot be achieved
Solution Approach 1:
The system performs preliminary clustering of players based on operation patterns before cheating occurs or as it emerges. By pre-organizing players into groups with similar behaviors and establishing baseline operation patterns, the system can immediately identify deviations when cheating starts, enabling rapid isolation without lengthy analysis delays
Solution Approach 2:
The system periodically re-clusters player groups and updates operation pattern analysis at regular intervals. This periodic reassessment ensures that emerging cheating patterns are detected quickly while maintaining system performance through structured, rhythmic analysis cycles rather than continuous processing
3Measurement precision
If player operation data is analyzed in detail, then cheating patterns can be identified, but processing complexity increases
Solution Approach 1:
The system segments the player base into distinct groups based on operation patterns rather than analyzing all player data uniformly. By dividing players into clusters with similar behaviors and focusing analysis on inter-group differences rather than individual player details, the system achieves high detection precision while reducing overall processing complexity
Solution Approach 2:
The system transforms detailed operation data into simplified feature parameters that capture essential behavioral characteristics. By converting complex operation sequences into condensed feature representations for clustering analysis, the system maintains detection accuracy while significantly reducing data processing requirements
Data Source
AI summary
A history-information obtaining unit obtains operation history information from a plurality of player terminals. A feature-information generating unit generates, for each of a plurality of players, feature information representing a feature of operation in the form of a certain weighted value obtained from the operation history information. A clustering unit clusters the plurality of players on the basis of the individual feature information of the plurality of players. An other-player-information sending control unit executes control so as to send information about other players belonging to the same group to the individual player terminals of the clustered plurality of players, while prohibiting sending of information about other players belonging to other groups.