AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
This addresses cheating in competitive online games like Counter-Strike 2, offering a reproducible baseline, but it is incremental as it applies an existing method to a new domain.
The paper tackled cheat detection in Counter-Strike 2 by developing a transformer-based model, AntiCheatPT_256, which achieved 89.17% accuracy and 93.36% AUC on a test set using a new dataset of 795 matches.
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.