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A basic probability assignment methodology for unsupervised wireless intrusion detection

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journal contribution
posted on 31.07.2018, 10:17 by Ibrahim Ghafir, Kostas Kyriakopoulos, Francisco J. Aparicio-Navarro, Sangarapillai Lambotharan, Basil AsSadhan, Hamad BinSalleeh
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.

Funding

This work has been supported by the British Council UK-Gulf Institutional Link Grant and the Engineering and Physical Science Research Council (EPSRC) Grant number EP/R006385/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Access

Citation

GHAFIR, I. ...et al., 2018. A basic probability assignment methodology for unsupervised wireless intrusion detection. IEEE Access, 6, pp.40008-40023.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

24/06/2018

Publication date

2018

Notes

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

ISSN

2169-3536

Language

en

Licence

Exports