A statistical aimbot detection method for online FPS games

Yu, Su-Yang; Hammerla, Nils; Yan, Jeff; Andras, Peter

Abstract

First Person Shooter (FPS) is a popular genre in online gaming, unfortunately not everyone plays the game fairly, and this hinders the growth of the industry. The aiming robot (aimbot) is a common cheating mechanism employed in this genre, it differs from many other common online bots in that there is a human operating alongside the bot, and thus the in-game data exhibit both human and bot-like behaviour. The aimbot users can aim much better than the average player. However, there are also a large number of highly skilled players who can aim much better than the average player, some of these players have in the past been banned from servers due to false accusations from their peers. Therefore, it would be interesting to find out if and where the honest player's and the bot user's behaviour differ. In this paper we investigate the difference between the aiming abilities of aimbot users and honest human players. We introduce two novel features and have conducted an experiment using a modified open source FPS game. Our data shows that there is significant difference between behaviours of honest players and aimbot users. We propose a voting scheme to improve aimbot detection in FPS based on distribution matching, and have achieved approximately 93% in both True positive and True negative rates with one of our features.

Citation

Yu, S.-Y., Hammerla, N., Yan, J., & Andras, P. (2012, June). A statistical aimbot detection method for online FPS games. Presented at The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: The 2012 International Joint Conference on Neural Networks (IJCNN)
• Start Date: Jun 10, 2012
• End Date: Jun 15, 2012
• Online Publication Date: Jul 30, 2012
• Publication Date: 2012
• Deposit Date: Nov 17, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2161-4407
• Book Title: The 2012 International Joint Conference on Neural Networks (IJCNN)
• DOI: https://doi.org/10.1109/IJCNN.2012.6252489
• Public URL: http://researchrepository.napier.ac.uk/Output/2809229