Hybrid cryptographic and AI frameworks for cheat detection in online games

The exponential growth of multiplayer gaming has led to the formation of an arms race between anticheat and cheat developers, which has been increasing in pace at an exponential rate, creating a need for the implementation of robust anti-cheat mechanisms for maintaining fair play. While advanced cryptographic techniques are foundational, their application within anti-cheat systems has continued to evolve over the last few decades. This analysis goes over conventional anti-cheat methods such as signature-based detection and memory scanning whilst identifying their critical limitations in combating novel and adaptive threats. The analysis then shifts to modern, AI-driven solutions. These include computer vision-based offerings such as aimbot detection and behavioral analysis using deep learning models, which offer proactive and adaptive capabilities. As per the results gleaned from the comparative analysis performed, this work identifies a crucial gap in existing literature and proposes a novel-hybrid security framework model which synergizes the efficiency of conventional techniques when it comes to known exploits with the dynamic detection power of AI for zero-day threats. The proposed architecture integrates these components with cryptographic technologies such as Trusted Execution Environments (TEEs) for process isolation and Hierarchical Identity-Based Encryption (HIBE) for secure authentication to create a resilient, multilayered defense. Finally, future research directions are outlined. These include the integration of post-quantum cryptography for asset protection and the development of lightweight frameworks for mobile and VR platforms. This review serves as a technical reference and architectural blueprint for designing the next generation of secure, cheat resistant online gaming systems.