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dc.contributor.authorRatnayake, Deepthi N.
dc.contributor.authorKazemian, Hassan B.
dc.contributor.authorYusuf, Syed A.
dc.date.accessioned2019-06-19T17:21:09Z
dc.date.available2019-06-19T17:21:09Z
dc.date.issued2012-10-24
dc.identifier.citationRatnayake , D N , Kazemian , H B & Yusuf , S A 2012 , Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm . in Proceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT . SciTePress , pp. 345-350 , International Conference on Security and Cryptography, SECRYPT 2012 , Rome , Italy , 24/07/12 . https://doi.org/10.5220/0004077703450350
dc.identifier.citationconference
dc.identifier.isbn9789898565242
dc.identifier.otherPURE: 16355121
dc.identifier.otherPURE UUID: 75e3aa3a-1f4a-43ef-95b9-339d09ae9592
dc.identifier.otherScopus: 84867641594
dc.identifier.urihttp://hdl.handle.net/2299/21380
dc.description.abstractThe Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations.en
dc.format.extent6
dc.language.isoeng
dc.publisherSciTePress
dc.relation.ispartofProceedings of the International Conference on Security and Cryptography - Volume 1: SECRYPT
dc.rightsOpen
dc.subjectGenetic algorithms
dc.subjectNeural networks
dc.subjectProbe request flooding attacks
dc.subjectWlan security
dc.subjectComputer Networks and Communications
dc.subjectInformation Systems
dc.subjectCommunication
dc.titleImproved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithmen
dc.contributor.institutionSchool of Computer Science
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84867641594&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2012-10-24
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.5220/0004077703450350
rioxxterms.licenseref.uriOther
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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