Year: 2025
Type: conference-paper
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$\mathbf{7 9 5}$matches. Using this dataset,$\mathbf{9 0, 7 0 7}$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.
Source: IEEE Conference on Games (CoG)
Authors: Mille Mei Zhen Loo, Gert Luẑkov, Paolo Burelli
Institution: IT University of Copenhagen
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413321744