用变压器模型分析游戏数据，精准识别反作弊行为。
AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
- 基于游戏行为数据构建变压器模型检测作弊
- 在795场对战数据上达到89.17%准确率
- 公开数据集，适合安全与游戏研究者使用
在线视频游戏中作弊行为破坏了游戏体验的公正性。传统反作弊系统（如VAC）难以跟上不断演变的作弊手段，且常伴随对用户系统的侵入性操作。本文提出AntiCheatPT_256，一种基于变压器的机器学习模型，用于通过游戏行为数据检测《反恐精英2》中的作弊行为。为此，我们公开发布了一个包含795场对战的标注数据集CS2CD。基于该数据集，生成了90,707个上下文窗口，并通过数据增强缓解类别不平衡问题。该模型在未增强测试集上实现89.17%的准确率和93.36%的AUC值。该方法强调可复现性与实际应用价值，为数据驱动的反作弊研究提供了稳健基准。
原文摘要 · Abstract (English)
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.
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