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

TL;DR

This paper introduces AntiCheatPT, a transformer-based machine learning model trained on a new dataset to detect cheating in Counter-Strike 2, achieving high accuracy and AUC, and emphasizing reproducibility and real-world application.

Contribution

The paper presents AntiCheatPT extsubscript{256}, a novel transformer-based cheat detection model, along with the publicly released CS2CD dataset for Counter-Strike 2 gameplay analysis.

Findings

Achieved 89.17 ext{ extperthousand} accuracy on cheat detection

Reached 93.36 ext{ extperthousand} AUC score

Demonstrated robustness with data augmentation techniques

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.