802.11 De-authentication Attack Detection using
Genetic Programming
P. LaRoche, und A. Zincir-Heywood. Proceedings of the 9th European Conference on Genetic
Programming, Volume 3905 von Lecture Notes in Computer Science, Seite 1--12. Budapest, Hungary, Springer, (10 - 12 April 2006)
Zusammenfassung
This paper presents a genetic programming approach to
detect deauthentication attacks on wireless networks
based on the 802.11 protocol. To do so we focus on
developing an appropriate fitness function and feature
set. Results show that the intrusion system developed
not only performs incredibly well - 100 percent
detection rate and 0.5 percent false positive rate -
but also developed a solution that is general enough to
detect similar attacks, such as disassociation attacks,
that were not present in the training data.
%0 Conference Paper
%1 eurogp06:LaRocheZincir-Heywood
%A LaRoche, Patrick
%A Zincir-Heywood, A. Nur
%B Proceedings of the 9th European Conference on Genetic
Programming
%C Budapest, Hungary
%D 2006
%E Collet, Pierre
%E Tomassini, Marco
%E Ebner, Marc
%E Gustafson, Steven
%E Ekárt, Anikó
%I Springer
%K algorithms, genetic programming
%P 1--12
%T 802.11 De-authentication Attack Detection using
Genetic Programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050001.pdf
%V 3905
%X This paper presents a genetic programming approach to
detect deauthentication attacks on wireless networks
based on the 802.11 protocol. To do so we focus on
developing an appropriate fitness function and feature
set. Results show that the intrusion system developed
not only performs incredibly well - 100 percent
detection rate and 0.5 percent false positive rate -
but also developed a solution that is general enough to
detect similar attacks, such as disassociation attacks,
that were not present in the training data.
%@ 3-540-33143-3
@inproceedings{eurogp06:LaRocheZincir-Heywood,
abstract = {This paper presents a genetic programming approach to
detect deauthentication attacks on wireless networks
based on the 802.11 protocol. To do so we focus on
developing an appropriate fitness function and feature
set. Results show that the intrusion system developed
not only performs incredibly well - 100 percent
detection rate and 0.5 percent false positive rate -
but also developed a solution that is general enough to
detect similar attacks, such as disassociation attacks,
that were not present in the training data.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Budapest, Hungary},
author = {LaRoche, Patrick and Zincir-Heywood, A. Nur},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2dc0e8e5c20816161e381912335b6cc3c/brazovayeye},
booktitle = {Proceedings of the 9th European Conference on Genetic
Programming},
editor = {Collet, Pierre and Tomassini, Marco and Ebner, Marc and Gustafson, Steven and Ek\'art, Anik\'o},
interhash = {5145584ac961ea8225be513d14024315},
intrahash = {dc0e8e5c20816161e381912335b6cc3c},
isbn = {3-540-33143-3},
keywords = {algorithms, genetic programming},
month = {10 - 12 April},
notes = {Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006},
organisation = {EvoNet},
pages = {1--12},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:45:10.000+0200},
title = {802.11 De-authentication Attack Detection using
Genetic Programming},
url = {http://link.springer.de/link/service/series/0558/papers/3905/39050001.pdf},
volume = 3905,
year = 2006
}