Browse by author
Lookup NU author(s): Su-Yang Yu,
Dr Jeff Yan,
Dr Peter Andras
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Yu SY, Hammerla N, Yan J, Andras P
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Joint Conference on Neural Networks (IJCNN)
Year of Conference: 2012
Library holdings: Search Newcastle University Library for this item