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

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  • Su Yang Yu
  • Nils Hammerla
  • Jeff Yan
  • Peter Andras
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Publication date2012
Host publicationNeural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationBerlin
PublisherSpringer Verlag
Pages654-661
Number of pages8
ISBN (Electronic)9783642345005
ISBN (Print)9783642344992
<mark>Original language</mark>English
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 12/11/201215/11/2012

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7667
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Neural Information Processing, ICONIP 2012
Country/TerritoryQatar
CityDoha
Period12/11/1215/11/12

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.