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.
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LaRoche, P., Zincir-Heywood, A.N. (2006). 802.11 De-authentication Attack Detection Using Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_1
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DOI: https://doi.org/10.1007/11729976_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
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