XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games

2026-01-26

AI Summary

This work addresses the longstanding challenges in detecting aimbot cheats in first-person shooter (FPS) games—namely poor generalization, high computational overhead, insufficient detection performance, and lack of interpretability. To this end, we propose XGuardian, a server-side, interpretable AI-powered anti-cheat system that leverages only two universal raw inputs from FPS games: pitch and yaw angles. By constructing temporal features to model anomalous aiming trajectories, XGuardian eliminates the need for game-specific customization, enabling cross-game generalization while achieving high detection accuracy, low latency, and transparent decision-making. Extensive evaluation on large-scale real-world datasets from Counter-Strike 2 and two other FPS titles demonstrates that XGuardian significantly outperforms existing approaches. The system and datasets are publicly released to foster further research.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Aim-assist cheats are the most prevalent and infamous form of cheating in First-Person Shooter (FPS) games, which help cheaters illegally reveal the opponent's location and auto-aim and shoot, and thereby pose significant threats to the game industry. Although a considerable research effort has been made to automatically detect aim-assist cheats, existing works suffer from unreliable frameworks, limited generalizability, high overhead, low detection performance, and a lack of explainability of detection results. In this paper, we propose XGuardian, a server-side generalized and explainable system for detecting aim-assist cheats to overcome these limitations. It requires only two raw data inputs, pitch and yaw, which are all FPS games'must-haves, to construct novel temporal features and describe aim trajectories, which are essential for distinguishing cheaters and normal players. XGuardian is evaluated with the latest mainstream FPS game CS2, and validates its generalizability with another two different games. It achieves high detection performance and low overhead compared to prior works across different games with real-world and large-scale datasets, demonstrating wide generalizability and high effectiveness. It is able to justify its predictions and thereby shorten the ban cycle. We make XGuardian as well as our datasets publicly available.

Problem

• aim-assist cheats
• FPS games
• cheat detection
• generalizability
• explainability

Innovation

• explainable AI
• generalized anti-cheat
• aim-assist detection
• temporal features
• FPS game security