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    WiFi Anomaly Behavior Analysis Based Intrusion Detection Using Online Learning

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    Author
    Torrres, Anibal
    Advisor
    Satam, Pratik
    Bose, Tamal
    Affiliation
    Department of Electrical and Computer Engineering, University of Arizona
    Issue Date
    2021-10
    
    Metadata
    Show full item record
    Citation
    Torrres, A. (2021). WiFi Anomaly Behavior Analysis Based Intrusion Detection Using Online Learning. International Telemetering Conference Proceedings, 56.
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    URI
    http://hdl.handle.net/10150/666297
    Additional Links
    http://www.telemetry.org/
    Abstract
    With the rise of the Internet of Things (IoT), wireless networks like WiFi are ubiquitous in today’s world. The widespread adoption of WiFi networks leads to correspondingly widespread attacks on these networks. WiFi attacks exploit vulnerabilities in the physical layer or the datalink layer specification of the protocol, making detecting and stopping these threats a challenging task as encryption based security solutions are harder to deploy. In this paper, we measure the performance of online learning classifiers, specifically Hoeffding Tree (HT), K-nearest neighbours (KNN), Accuracy Weighted Ensemble Classifier(AWEC),and Half-Spaced Treed(HST), to detect attacks on WiFi networks. The experimental evaluation is performed on Aegean WiFi Intrusion Dataset 2 (AWID 2) and Aegean WiFi Intrusion Dataset 3 (AWID 3). Experimental evaluations show that HT has the best accuracy of 98% for both the datasets, but takes training on 300,000 packets to reach this performance. While HST and KNN converged more quickly, they were never more accurate than HT after 70,000 iterations or AWEC after 140,000 iterations. For this reason HT and AWEC are the most highly rated out of the classifiers examined.
    Type
    Proceedings
    text
    Language
    en
    ISSN
    1546-2188
    0884-5123
    0074-9079
    Sponsors
    International Foundation for Telemetering
    Collections
    International Telemetering Conference Proceedings, Volume 56 (2021)

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