Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix

Yu, Su Yang and Hammerla, Nils and Yan, Jeff and Andras, Peter (2012) Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. In: Neural Information Processing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer Verlag, QAT, pp. 654-661. ISBN 9783642344992

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Abstract

Online gaming is very popular and has gained some recognition as the so called e-sport over the last decade. However, in particular First Person Shooter (FPS) games suffer from the development of sophisticated cheating methods such as aiming robots (aimbot), which can boost the players ability to acquire and track targets by the illicit use of internal game states. This not only gives an obvious unfair advantage to the cheater, but has negative impact on the gaming experience of honest players. In this paper we present a novel supervised method based on distribution comparison matrices that shows very promising performance in the identification of players that use such aimbots. It extends our previous work in which two features were identified and shown to have good predictive performance. The proposed method is further compared with other classification techniques such as Support Vector Machines (SVM). Overall we achieve true positive and true negatives rates well above 98% with low computational requirements.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2614
Subjects:
ID Code:
77696
Deposited By:
Deposited On:
14 Jan 2016 15:26
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Apr 2020 07:29