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

arXiv: https://arxiv.org/abs/2508.06348 · HTML: https://mlsanity.com/paper/2508.06348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters](https://mlsanity.com/paper/2205.07060/) (2022)
- [Robust Vision-Based Cheat Detection in Competitive Gaming](https://mlsanity.com/paper/2103.10031/) (2021)

## Built on by (most cited first, 1 of 1)

- [XGuardian: Towards Explainable and Generalized AI Anti-Cheat on FPS Games](https://mlsanity.com/paper/2601.18068/) (2026)

## Abstract (arXiv)

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 AntiCheatPT256, 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.