AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games

2025-08-08

AI Summary

To address the challenges of non-intrusive cheating detection and poor generalizability in competitive gaming, this paper proposes AntiCheatPT_256—the first Transformer-based anti-cheat framework. Methodologically, we construct CS2CD, a large-scale, high-quality labeled dataset, and introduce context-window modeling alongside targeted data augmentation and resampling strategies, enabling supervised binary classification solely from non-intrusive gameplay sequences. Our key contributions are: (1) open-sourcing the CS2CD dataset to foster reproducible research; (2) empirically validating the efficacy of Transformers for long-sequence behavioral modeling in cheating detection; and (3) achieving 89.17% accuracy and 93.36% AUC on an unenhanced test set—substantially outperforming conventional rule-based systems and lightweight models—demonstrating strong real-world robustness and practical deployability.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Multiagent Systems: Adversarial Agents

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
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Cheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems. This paper presents AntiCheatPT_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset of 795 matches. Using this dataset, 90,707 context windows were created and subsequently augmented to address class imbalance. The transformer model, trained on these windows, achieved an accuracy of 89.17% and an AUC of 93.36% on an unaugmented test set. This approach emphasizes reproducibility and real-world applicability, offering a robust baseline for future research in data-driven cheat detection.

Problem

• Detect cheating in online games using transformer models
• Address limitations of current anti-cheat systems like VAC
• Provide a reproducible dataset for future cheat detection research

Innovation

• Transformer-based model for cheat detection
• Publicly released labeled CS2CD dataset
• Achieves 89.17% accuracy in testing