AntiCheatPT：一种基于Transformer的竞技电脑游戏作弊检测方法
AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
AI总结：
针对现有反作弊系统难兼顾非侵入性与作弊手段迭代的问题，提出基于Transformer的《CS2》作弊检测模型AntiCheatPT_256，发布含795场对局的标注数据集CS2CD，经数据增强训练后模型准确率达89.17%，为相关研究提供可靠基线。
摘要：
在线电子游戏中的作弊行为会破坏游戏体验的公平性。VAC（Valve反作弊系统）等反作弊系统面临着巨大挑战：既要跟上不断演变的作弊手段，又不能对用户系统采取侵入性措施。本文提出AntiCheatPT_256，这是一款基于Transformer的机器学习模型，旨在利用游戏数据检测《Counter-Strike 2》中的作弊行为。为此，我们推出并公开发布了CS2CD：一个包含795场对局的标注数据集。基于该数据集我们构建了90707个上下文窗口，随后对其进行数据增强以解决类别不平衡问题。在这些窗口上训练的Transformer模型，在未增强的测试集上取得了89.17%的准确率和93.36%的AUC。该方法强调可复现性和实际适用性，为数据驱动的作弊检测领域的未来研究提供了可靠的基线。
英文摘要：
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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