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    A basic probability assignment methodology for unsupervised wireless intrusion detection

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    Ghafir_IEEE_Access_2018 (1.883Mb)
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    Publication date
    2018-07-11
    Author
    Ghafir, Ibrahim
    Kyriakopoulos, K.G.
    Aparicio-Navarro, F.J.
    Lambotharan, S.
    Assadhan, B.
    Binsalleeh, A.H.
    Keyword
    Basic probability assignment
    Data fusion
    Dempster-Shafer theory
    Intrusion detection system
    Local reachability density
    Network security
    Probability density function
    Wireless injection attacks
    Rights
    This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    The broadcast nature of wireless local area networks 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. Since most attacks manifest themselves through different metrics, current IDSs should leverage a cross-layer approach to help toward improving the detection accuracy. The data fusion technique based on the Dempster–Shafer (D-S) theory has been proven to be an efficient 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, the categorical probability mass function, and the local reachability density. 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.
    URI
    http://hdl.handle.net/10454/17616
    Version
    Published version
    Citation
    Ghafir I, Kyriakopoulos KG, Aparicio-Navarro FJ et al (2018) A basic probability assignment methodology for unsupervised wireless intrusion detection. IEEE Access. 6: 40008-40023.
    Link to publisher’s version
    https://doi.org/10.1109/ACCESS.2018.2855078
    Type
    Article
    Collections
    Engineering and Informatics Publications

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