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Video game cheating seriously threatens fair play and erodes player confidence, underscoring the need for sophisticated detection and prevention techniques. Recent investigations focus on multi-layered anti-cheat systems that include server-side validation, behavioral monitoring, predictive analytics, and memory integrity checks. Data-driven analysis of player activity is used to address new issues in Web3 games. Cloud-based and massively multiplayer online games benefit from improved resource provisioning and anti-cheat detection thanks to predictive modeling. While specific works support scalable monitoring methods inspired by network security paradigms, existing studies in video game server security show the value of real-time forecasting in maximizing server performance. As essential problems, there is currently a focus on limiting false positives, balancing detection accuracy, and resolving privacy concerns. In order to develop anti-cheat solid solutions and guarantee sustainability and fairness in online gaming, this paper emphasizes the significance of combining machine learning, cybersecurity, and ethical issues.

computer networks; video games; anti-cheating; server prediction; server telemetry; data protection