Gynopticon: Consensus-Based Cheating Detection System for Competitive Games
Jeuk Kang, Jungheum Park

TL;DR
GYNOPTICON is a novel, privacy-preserving cheating detection system for competitive online games that uses user consensus through client-server voting to identify cheaters effectively.
Contribution
It introduces a lightweight, consensus-based framework combining client detection and server voting, addressing privacy concerns and enhancing cheating detection in competitive games.
Findings
Effective detection of cheaters in FPS games
Reduces privacy risks compared to kernel-level solutions
Proven feasibility through simulation and real-world testing
Abstract
Cheating in online games poses significant threats to the gaming industry, yet most prior research has concentrated on Massively Multiplayer Online Role-Playing Games (MMORPGs). Competitive genres-such as Multiplayer Online Battle Arena (MOBA), First Person Shooter (FPS), Real Time Strategy (RTS), and Action games-remain underexplored due to the difficulty of detecting cheating users and the demand for complex data and techniques. To address this gap, many game companies rely on kernel-level anti-cheat solutions, which, while effective, raise serious concerns regarding user privacy and system security. In this paper, we propose GYNOPTICON, a novel cheating detection framework that leverages user consensus to identify abnormal behavior. GYNOPTICON integrates a lightweight client-side detection mechanism with a server-side voting system: when suspicious activity is identified, clients…
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Taxonomy
TopicsArtificial Intelligence in Games · Mobile Crowdsensing and Crowdsourcing · Peer-to-Peer Network Technologies
