Preventing bots from playing online games
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
A systematic classification of cheating in online games
NetGames '05 Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games
Detection of MMORPG bots based on behavior analysis
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Identifying MMORPG bots: a traffic analysis approach
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing applications in network intrusion detection systems
Game bot identification based on manifold learning
Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games
Server-Side Bot Detection in Massively Multiplayer Online Games
IEEE Security and Privacy
Battle of Botcraft: fighting bots in online games with human observational proofs
Proceedings of the 16th ACM conference on Computer and communications security
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As the online game industry expands, detecting and preventing cheating in games is an increasingly important research topic. Some forms of cheating, such as the use of game bots (auto-playing game clients), are particularly challenging to identify because game bots do not violate any of the game rules; rather, they simply mimic human behavior to play the game without human intervention. The use of bots introduces fairness issues to online games, and therefore robust schemes for detecting game bots are strongly demanded. In this paper, we tackle with bots in rhythm games, which feature gameplay that incorporates eye and body coordination with music, usually a popular song. Bot detection in rhythm games is especially challenging compared with in other game genres because little information is available to distinguish the responses made by a human player from a bot. Based on the long-memoryness of the time series formed by human players' response errors to stimuli, we propose a scheme to detect the presence of human coordination mechanisms during gameplay. Based on a set of traces collected from human players and real-life game bots, we show that our scheme can accurately detect the use of game bots despite of game difficulty levels.