• Login
    View Item 
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    •   DORA Home
    • Faculty of Computing, Engineering and Media
    • School of Computer Science and Informatics
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Basic Probability Assignment Methodology for Unsupervised Wireless Intrusion Detection

    Thumbnail
    View/Open
    A Basic Probability Assignment Methodology for Unsupervised Wireless Intrusion Detection.pdf (1.883Mb)
    Date
    2018-07-11
    Author
    Ghafir, I.;
    Kyriakopoulos, Konstantinos;
    Aparicio-Navarro, Francisco J.;
    Lambotharan, Sangarapillai;
    AsSadhan, B.;
    BinSalleeh, H.
    Metadata
    Show attachments and full item record
    Abstract
    The 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.
    Description
    open access article
    Citation : Ghafir, 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.
    URI
    http://hdl.handle.net/2086/16440
    https://dspace.lboro.ac.uk/2134/34208
    DOI
    https://doi.org/10.1109/access.2018.2855078
    ISSN : 2169-3536
    Collections
    • School of Computer Science and Informatics [2970]

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary
     

     

    Browse

    All of DORACommunities & CollectionsAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission DateThis CollectionAuthorsTitlesSubjects/KeywordsResearch InstituteBy Publication DateBy Submission Date

    My Account

    Login

    Submission Guide | Reporting Guide | Reporting Tool | DMU Open Access Libguide | Take Down Policy | Connect with DORA
    DMU LIbrary