Online Game Collusion Detection via Anomaly Analysis
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Solution Overview
Problem
Online gaming environments face challenges in detecting collusion among players, which leads to unfair advantages, as existing systems lack effective methods to identify and address coordinated behavior between entities.
Innovation Solution
A collusion detection system that analyzes social relationships and in-game behavior data to generate feature sets, which are then input into an anomaly detection algorithm to identify suspected colluding teams or players, enabling automatic or reviewed actions such as disqualification or banning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems are used for player behavior monitoring, then basic game operations can proceed, but collusion among players cannot be effectively detected
Solution Approach 1:
The detection system is segmented into multiple independent modules: a data collection module that gathers raw behavior data, a feature extraction module that processes data into collusion indicators, and an anomaly detection module that analyzes patterns. This segmentation allows the system to achieve high detection accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces feature extraction as an intermediary layer between raw data collection and anomaly detection. This intermediary processes complex multi-player behavior data into standardized collusion indicators, enabling accurate detection without requiring the entire system to handle the full complexity of raw behavioral data simultaneously.
2Productivity
If manual review of player behavior is performed, then collusion can be identified, but the process is time-consuming and inefficient
Solution Approach 1:
The system implements self-service detection by automatically collecting behavior data, extracting collusion features, and identifying anomalous patterns without requiring continuous manual intervention. The automated anomaly detection algorithm processes player interactions in real-time, enabling the system to detect and flag collusion incidents instantly, thereby eliminating time losses associated with manual review processes.
3Measurement precision
If comprehensive behavior data is collected from all players, then detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The feature extraction module selectively extracts only the most relevant collusion indicators from comprehensive behavior data, such as coordinated attack patterns, unusual communication frequencies, and suspicious team formation behaviors. This extraction process maintains high detection precision by focusing on key indicators while reducing processing complexity by eliminating irrelevant data elements.
Solution Approach 2:
The system applies different processing strategies to different types of behavior data based on their relevance to collusion detection. High-priority data such as coordinated attack sequences receive intensive analysis, while routine gameplay actions use simplified processing. This local quality approach maintains detection precision for critical indicators while reducing overall processing complexity.
Data Source
AI summary
A collusion detection system may detect collusion between entities participating in online gaming. The collusion detection system may identify a plurality of entities associated with and opponents within an instance of an online game, determine social data associated with the plurality of entities, determine in-game behavior data associated with the plurality of entities, and determine, for one or more pairings of the plurality of entities, respective pairwise feature sets based at least in part on the social data and the in-game behavior data. The collusion detection system may then perform anomaly detection on the respective pairwise feature sets and, in response to the anomaly detection detecting one or more anomalous pairwise feature sets, output one or more suspect pairings of the plurality of entities corresponding to the one or more anomalous pairwise feature sets as suspected colluding pairings.