Standard
Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. / Yu, Su Yang; Hammerla, Nils
; Yan, Jeff et al.
Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Berlin: Springer Verlag, 2012. p. 654-661 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7667 ).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Yu, SY, Hammerla, N
, Yan, J & Andras, P 2012,
Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. in T Huang, Z Zeng, C Li & CS Leung (eds),
Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7667 , Springer Verlag, Berlin, pp. 654-661, 19th International Conference on Neural Information Processing, ICONIP 2012, Doha, Qatar,
12/11/12.
https://doi.org/10.1007/978-3-642-34500-5_77APA
Yu, S. Y., Hammerla, N.
, Yan, J., & Andras, P. (2012).
Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.),
Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V (pp. 654-661). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7667 ). Springer Verlag.
https://doi.org/10.1007/978-3-642-34500-5_77Vancouver
Yu SY, Hammerla N
, Yan J, Andras P.
Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. In Huang T, Zeng Z, Li C, Leung CS, editors, Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V. Berlin: Springer Verlag. 2012. p. 654-661. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-34500-5_77
Author
Yu, Su Yang ; Hammerla, Nils
; Yan, Jeff et al. /
Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix. Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part V. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Berlin : Springer Verlag, 2012. pp. 654-661 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Bibtex
@inproceedings{e2b2a26997324e338ed97e4fd5bfef63,
title = "Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix",
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.",
keywords = "Cheating Detection, Computational Intelligence, Computer Games, Distribution Comparison, First Person Shooters, Game Bots",
author = "Yu, {Su Yang} and Nils Hammerla and Jeff Yan and Peter Andras",
year = "2012",
doi = "10.1007/978-3-642-34500-5_77",
language = "English",
isbn = "9783642344992",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "654--661",
editor = "Tingwen Huang and Zhigang Zeng and Chuandong Li and Leung, {Chi Sing}",
booktitle = "Neural Information Processing",
note = "19th International Conference on Neural Information Processing, ICONIP 2012 ; Conference date: 12-11-2012 Through 15-11-2012",
}
RIS
TY - GEN
T1 - Aimbot detection in online FPS games using a heuristic method based on distribution comparison matrix
AU - Yu, Su Yang
AU - Hammerla, Nils
AU - Yan, Jeff
AU - Andras, Peter
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Cheating Detection
KW - Computational Intelligence
KW - Computer Games
KW - Distribution Comparison
KW - First Person Shooters
KW - Game Bots
U2 - 10.1007/978-3-642-34500-5_77
DO - 10.1007/978-3-642-34500-5_77
M3 - Conference contribution/Paper
AN - SCOPUS:84869051836
SN - 9783642344992
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 654
EP - 661
BT - Neural Information Processing
A2 - Huang, Tingwen
A2 - Zeng, Zhigang
A2 - Li, Chuandong
A2 - Leung, Chi Sing
PB - Springer Verlag
CY - Berlin
T2 - 19th International Conference on Neural Information Processing, ICONIP 2012
Y2 - 12 November 2012 through 15 November 2012
ER -
