Identify As A Human Does: A Pathfinder of Next-Generation Anti-Cheat Framework for First-Person Shooter Games

2024-09-23

arXiv.org

AI Summary

FPS cheating severely undermines competitive fairness and ecosystem health. Existing anti-cheat solutions suffer from client-side hardware dependencies, elevated security risks, unreliable server-side detection, and a lack of large-scale real-world data. This paper proposes HAWK, a server-side anti-cheat framework for CS:GO. Methodologically, it introduces a novel multi-perspective behavioral feature modeling approach; constructs the first large-scale, real-world FPS cheating dataset—comprising diverse cheat types and difficulty levels; and designs a rule-augmented machine learning decision pipeline (XGBoost/LightGBM) that jointly leverages multi-source game-state features and real-time behavioral sequence analysis. Evaluation demonstrates that HAWK significantly reduces inference overhead and ban latency, decreases manual review volume by 72%, and successfully detects stealthy cheaters evading Valve Anti-Cheat (VAC).

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts' identification process, leverages novel multi-view features, and it is equipped with a well-defined workflow. The authors evaluate HAWK with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times compared to the in-use anti-cheat, a significant reduction in manual labor, and the ability to capture cheaters who evaded official inspections.

Problem

• Addressing cheating threats in first-person shooter games using machine learning
• Overcoming limitations of existing anti-cheat solutions with server-side framework
• Developing comprehensive detection system that mimics human expert identification

Innovation

• Server-side machine learning framework for FPS games
• Multi-view features mimicking human expert identification
• Large real-world dataset evaluation with reduced ban times