The Evolution of Anti-Cheat Systems: A Literature Review of AI-Driven and Behavioral Approaches

Abstract

With the rise in popularity of online video games, cheating has become a real problem that harms the gaming experience and damages the industry’s reputation. Traditional methods for catching cheaters often lag behind in detection and punishment. These methods can also be easily bypassed by new cheating tools. In response, recent research has looked into using artificial intelligence (AI) and machine learning to create better and more proactive anti-cheat systems. This review combines findings from seven key papers that suggest different AI-based strategies to fight cheating in first-person shooter (FPS) games and other online games. The strategies discussed include analyzing player behavior and in-game performance data, using visual recognition, and employing adversarial attacks. The papers show how these new methods could lead to faster and more precise cheat detection. They also address challenges like data access, the need for transparency, and the ongoing back-and-forth with cheating tactics.

Introduction

The online gaming industry is a multi-billion-dollar market where fair competition is essential. Cheating—particularly in competitive games like FPS titles—undermines trust and player satisfaction. Traditional kernel-based anti-cheat systems (e.g., Easy Anti-Cheat, Riot Vanguard) monitor low-level processes but have limitations including privacy concerns, performance issues, security vulnerabilities, and susceptibility to evasion.

Recent research has shifted toward AI and data-driven anti-cheat methods, using behavioral signatures, input patterns, and vision-based detection. Key approaches include:

1. Explainable Multi-View Detection (2020): Uses LSTMs and Transformers to analyze static and dynamic player data, providing explainable results. Limitation: high complexity and dependence on rich backend data.

2. Vision-Based Deep Learning (2021): Detects impossible player actions using object detection (YOLOv5) on screenshots. Limitation: high computational cost and potential false positives.

3. Input Pattern Analysis (2021): Treats mouse and keyboard inputs as multivariate time series for CNN classification, detecting aimbots/triggerbots with high accuracy. Limitation: ineffective against informational cheats like ESP.

4. Mouse Movement CNN Detection (2023): Converts 1D mouse inputs into 2D images for CNN classification, achieving over 99% accuracy. Limitation: narrowly focused on a single aimbot type.

5. Proactive Vision-Based Defense (2023): Uses adversarial perturbations to disrupt cheat tools without affecting legitimate players. Limitation: requires deep modification of the game engine.

6. VESPA System (2023): Hybrid supervised/unsupervised visual analysis for ESP cheats, integrated with human review. Limitation: reactive, post-match detection.

7. HAWK Framework (2024): Human-inspired post-game replay analysis using LSTMs and ensemble classifiers for anomaly detection. Limitation: reactive, cannot prevent cheating in real-time.

Overall, modern anti-cheat research emphasizes AI-driven, behavior- and vision-based detection systems that improve accuracy, fairness, and adaptability, though many approaches remain reactive or computationally intensive.

Conclusion

The research reviewed here shows a clear trend in anti- cheat methods. There is a noticeable shift away from in- trusive, kernel-based techniques, which pose privacy and stability risks, toward smarter, AI-driven, interpretable, and adaptable solutions. These systems use behavioral patterns, visual data, and human-computer interaction patterns to detect cheating with greater accuracy and complexity. However, a key limitation remains in the literature: these approaches are mostly reactive. They are built to identify cheating only after it has happened, leading to a constant battle with cheat developers. Detection methods need frequent updates to handle new exploits. This reactive stance under- scores the need to move from just identifying bad behavior to preventing it from the start. Our work addresses this gap by suggesting a capability architecture with memory compartmentalization. Rather than focusing on detection after the fact, this method imposes strict controls on memory access at the architectural level. This approach shifts the focus from detection to prevention, aiming to make entire categories of client-side exploits tech- nically impossible. The future of strong game security lies in a hybrid strat- egy. This approach would use a capability architecture as a preventative base, making many common, memory- based exploits architecturally unfeasible. On top of this se- cure foundation, the adaptive AI-driven detection methods discussed in this review would act as a crucial secondary system. This system would identify new threats or hardware- based cheats that could bypass the architectural safeguards. This two-pronged strategy, which combines architectural pre- vention with behavioral detection, offers the most effective and forward-thinking path for ensuring fairness and trust in competitive gaming.

References

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Copyright

Copyright © 2025 Rylan Mathews Thomas, Jose V Francis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.