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dc.contributor.authorGhafir, I.en
dc.contributor.authorKyriakopoulos, Konstantinosen
dc.contributor.authorAparicio-Navarro, Francisco J.en
dc.contributor.authorLambotharan, Sangarapillaien
dc.contributor.authorAsSadhan, B.en
dc.contributor.authorBinSalleeh, H.en
dc.date.accessioned2018-08-07T14:49:05Z
dc.date.available2018-08-07T14:49:05Z
dc.date.issued2018-07-11
dc.identifier.citationGhafir, I., Kyriakopoulos, K.G., Aparicio-Navarro, F.J., Lambotharan, S., AsSadhan, B.,BinSalleeh, H. (2018) A basic probability assignment methodology for unsupervised wireless intrusion detection. IEEE Access, 6, pp. 1-16.en
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2086/16440
dc.identifier.urihttps://dspace.lboro.ac.uk/2134/34208
dc.descriptionopen access articleen
dc.description.abstractThe broadcast nature of Wireless Local Area Networks (WLANs) has made them prone to several types of wireless injection attacks, such as Man-in-the-Middle (MitM) at the physical layer, deauthentication and rogue access point attacks. The implementation of novel Intrusion Detection Systems (IDSs) is fundamental to provide stronger protection against these wireless injection attacks. Because most attacks manifest themselves through different metrics, current IDSs should leverage a cross-layer approach to help towards improving the detection accuracy. The data fusion technique based on Dempster-Shafer (D-S) theory has been proven to be an efficient data fusion technique to implement the cross-layer metric approach. However, the dynamic generation of the Basic Probability Assignment (BPA) values used by D-S is still an open research problem. In this paper, we propose a novel unsupervised methodology to dynamically generate the BPA values, based on both the Gaussian and exponential probability density functions (pdf), the categorical probability mass function (pmf), and the local reachability density (lrd). Then, D-S is used to fuse the BPA values to classify whether the Wi-Fi frame is normal (i.e. non-malicious) or malicious. The proposed methodology provides 100% True Positive Rate (TPR) and 4.23% False Positive Rate (FPR) for the MitM attack, and 100% TPR and 2.44% FPR for the deauthentication attack, which confirm the efficiency of the dynamic BPA generation methodology.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectBasic probability assignmenten
dc.subjectData fusionen
dc.subjectDempster-Shafer theoryen
dc.subjectIntrusion detection systemen
dc.subjectLocal reachability densityen
dc.subjectNetwork securityen
dc.subjectProbability density functionen
dc.subjectWireless injection attacksen
dc.titleA Basic Probability Assignment Methodology for Unsupervised Wireless Intrusion Detectionen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/access.2018.2855078
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/R006385/1en
dc.projectid279339985en
dc.cclicenceCC BYen
dc.date.acceptance2018-06-24en
dc.exception.reasonThe output was published as gold open accessen


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