Cheating poses a significant threat to the Multiplayer Online Games (MOG) industry by degrading player satisfaction and undermining the fairness in competitive gaming. Despite efforts to develop mitigation techniques, cheating remains difficult to detect and prevent in practice. In particular, a class of cheats based on network flow disruption remains unsolvable. To find out how to detect such attacks we need access to representative labelled data. However, no such dataset exists. To address this gap, we leverage an experimental framework that combines a multiplayer online game with a plug-in capable of both reproducing cheating attacks and collecting logs at two levels: network and application-layer. This paper presents a dataset compiling records of game sessions played by both real players and automated game clients, with cheating actions explicitly logged. To the best of our knowledge, this is the first dataset that provides logs of network flow disruption cheats. While it includes such network-based cheats, it is not limited to them and also contains records of more commonly studied cheats, such as aimbots and wallhacks. This dataset can be used by researchers in academia and industry seeking to develop cheating detection mechanisms for online games. Furthermore, it is designed to be evolutive and can be enriched by others creating their own data traces with the proposed framework.

翻译：作弊行为通过降低玩家满意度并破坏竞技游戏的公平性，对多人网络游戏（MOG）行业构成重大威胁。尽管已开发出缓解技术，但作弊在现实中仍难以检测和防范。尤其是一类基于网络流中断的作弊手段至今无法解决。为探究此类攻击的检测方法，我们需要获取具有代表性的标注数据，然而当前尚无此类数据集。为填补这一空白，我们利用一个实验框架，该框架将多人网络游戏与能够复制作弊攻击并同时收集网络层和应用层日志的插件相结合。本文呈现了一个数据集，其中包含真实玩家和自动化游戏客户端进行的游戏会话记录，并明确记录了作弊行为。据我们所知，这是首个提供网络流中断作弊日志的数据集。尽管它包含此类基于网络的作弊，但不仅限于此，还包含更常见作弊（如自瞄和透视）的记录。该数据集可供学术界和工业界的研究人员用于开发在线游戏作弊检测机制。此外，数据集设计为可演进式，其他人可通过所提框架创建自己的数据轨迹来丰富它。