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

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

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.

Cheating Detection, Computational Intelligence, Computer Games, Distribution Comparison, First Person Shooters, Game Bots
0302-9743
PART 5
654-661
Yu, Su Yang
bc8813ad-ba1e-47ec-b9a3-18f0aba19b78
Hammerla, Nils
702130fd-69a5-4790-9e9e-ac3eb8f874bd
Yan, Jeff
a2c03187-3722-46c8-b73b-439eb9d1a10e
Andras, Peter
e4f60324-9221-4e9a-b3d7-b9541eeb8802
Yu, Su Yang
bc8813ad-ba1e-47ec-b9a3-18f0aba19b78
Hammerla, Nils
702130fd-69a5-4790-9e9e-ac3eb8f874bd
Yan, Jeff
a2c03187-3722-46c8-b73b-439eb9d1a10e
Andras, Peter
e4f60324-9221-4e9a-b3d7-b9541eeb8802

Yu, Su Yang, Hammerla, Nils, 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 - 19th International Conference, ICONIP 2012, Proceedings. vol. 7667 LNCS, pp. 654-661 . (doi:10.1007/978-3-642-34500-5_77).

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: 2012
Venue - Dates: 19th International Conference on Neural Information Processing, ICONIP 2012, , Doha, Qatar, 2012-11-12 - 2012-11-15
Keywords: Cheating Detection, Computational Intelligence, Computer Games, Distribution Comparison, First Person Shooters, Game Bots

Identifiers

Local EPrints ID: 500834
URI: http://eprints.soton.ac.uk/id/eprint/500834
ISSN: 0302-9743
PURE UUID: 2092412a-7959-4cc5-9d1e-64edc6a9945d

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Date deposited: 13 May 2025 17:24
Last modified: 13 May 2025 17:24

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Contributors

Author: Su Yang Yu
Author: Nils Hammerla
Author: Jeff Yan
Author: Peter Andras

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