Esports Fraud Detection via Peripheral Interaction Profiling
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Solution Overview
Problem
Existing methods for detecting cheating in esports are inadequate as they often rely on specific game information, personal data, or hardware/software monitoring, which are not scalable or generic enough to cover diverse game types and cheating methods, and fail to detect subtle or real-time unsportsmanlike behaviors effectively.
Innovation Solution
A system that monitors player interaction with input peripherals (such as keyboards and mice) using statistical tools to build and compare interaction profiles, allowing for real-time detection of cheating behaviors without requiring specific game information or personal data, and is adaptable to any gaming apparatus or game type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing fraud detection methods use specific game information, personal data, or hardware/software monitoring, then detection capability is improved, but scalability and applicability across diverse game types deteriorates
Solution Approach 1:
The system uses a generic fraud detection approach that works across multiple game types by monitoring player behavior patterns rather than game-specific mechanics. The fraud detection module analyzes universal indicators such as response times, action frequencies, and behavioral anomalies that are applicable to any online game, making the system scalable and adaptable without requiring game-specific customization
Solution Approach 2:
The system changes the detection parameters from game-specific information to player behavior parameters. Instead of monitoring game state data or hardware configurations, the system tracks temporal patterns in player actions, response times, and interaction frequencies - parameters that remain consistent across different game types and can be universally analyzed for fraud detection
2Measurement precision
If existing methods monitor hardware/software activities or network activities, then detection precision is improved, but risk of interfering with apparatus performance increases
Solution Approach 1:
The system introduces an intermediary fraud detection module that operates at the network communication layer rather than directly monitoring hardware or software activities. This intermediary captures behavioral data from network traffic and player actions without requiring direct access to or interference with the game application or hardware, thus maintaining detection precision while avoiding performance interference
Solution Approach 2:
The system extracts only the necessary behavioral indicators from player actions and network traffic for fraud detection, rather than monitoring all hardware/software activities. By selectively extracting relevant temporal patterns and action frequencies from the data stream, the system achieves detection precision while minimizing the overhead and potential interference with game performance
3Measurement precision
If existing approaches rely on specific in-game information, then detection accuracy for known cheating methods is improved, but ability to detect subtle or new cheating behaviors deteriorates
Solution Approach 1:
The system establishes baseline behavioral patterns for legitimate players through preliminary analysis of normal gameplay data. By pre-defining what constitutes normal temporal patterns in player actions, response times, and interaction frequencies, the system can detect deviations indicating fraud without needing to know specific cheating methods in advance, enabling detection of both known and novel cheating behaviors
Solution Approach 2:
The system implements continuous feedback loops where detected fraud cases and emerging cheating patterns are fed back into the analysis model. This allows the system to adapt and refine its detection criteria over time, maintaining high accuracy for known cheating methods while becoming increasingly effective at identifying new and subtle fraudulent behaviors through pattern recognition
Data Source
AI summary
A method and apparatus to objectively identify and classify cheating behaviors in players of virtual games, namely identity fraud or any type of software-based fraud or other unsportsmanlike or unethical behaviors. A method to use that information to inform the players, the game, the game servers, or any other external entity of the cheating behaviors found, allowing these entities to trigger disciplinary or punitive actions as per the regulations of the game or competition.