The Battle Between Cybersecurity and Anti-Cheating Technologies in Online Games: The Threat of Cheating Software and Anti-Cheating Techniques

Abstract

This paper focuses on the attack surface of cheating threats in online games, providing a comprehensive overview of the problem from four layers: process layer, kernel layer, hardware layer, and visual layer. It then systematically classifies and compares the corresponding defense mechanisms for each of these four threat layers: client-side signature scanning forms the basis of static defense; kernel callback monitoring, memory forensics engines, and rootkit review cover deep system threats; server-side behavior modeling and deep learning detection frameworks (Hawk, XGUARDIAN) enable intelligent perception across the threat surface for behavioral anomalies; and adversarial proactive defense (Invisibility Cloak) and privacy-preserving local detection address the trade- off between visual AI cheating and privacy from different dimensions. This paper further identifies three core challenges currently facing this field: the software detection blind spot of hardware-level cheats, the structural contradiction between deep detection and user privacy, and the dual deficiencies in the cross-game generalization ability and interpretability of AI models. Future research can be conducted in three main directions: trusted hardware fusion, federated learning collaborative detection, and multimodal behavioral fingerprinting.