Cheating in Multiplayer Online Games: a Dataset

2026-06-04

AI Summary

This work addresses the challenge of detecting network flow disruption–based cheating in online multiplayer games, a task hindered by the absence of publicly available, accurately labeled datasets. To bridge this gap, the authors present the first open dataset that simultaneously captures network traffic and application-level logs from real gameplay sessions, with precise annotations for diverse cheating behaviors—including network flow disruption, aimbot, and wallhack. Notably, this dataset provides fine-grained labels specifically for network flow disruption attacks for the first time. Furthermore, the study introduces a scalable experimental framework designed to facilitate ongoing community contributions of new data. This resource establishes a foundational benchmark for both academic research and industrial development of effective anti-cheat mechanisms.

Abstract

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.

Problem

• cheating
• multiplayer online games
• network flow disruption
• dataset
• cheat detection

Innovation

• network flow disruption
• cheating detection
• multiplayer online games
• labeled dataset
• experimental framework