Gynopticon: Consensus-Based Cheating Detection System for Competitive Games

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 cast votes to the server, which aggregates them to establish consensus and distinguish cheaters from legitimate players. This architecture enables transparency, reduces reliance on intrusive monitoring, and mitigates privacy risks. We evaluate GYNOPTICON in both a controlled simulation and a real-world FPS environment. Simulation results verify its feasibility and requirements, while real-world experiments confirm its effectiveness in reliably detecting cheating users. Furthermore, we demonstrate the system's applicability and sustainability for long-term game management using public datasets. GYNOPTICON represents a user-driven, consensus-based alternative to conventional anti-cheat systems, offering a practical and privacy-preserving solution for competitive online games.