A Novel Technique to Discover De-Authentication DoS Attacks in 802.11 Wi-Fi Networks

Sudeesh Chouhan Sumit Sharma


Denial of Service (DoS) Attacks in 802.11 networks is mainly caused because of weaknesses of Media Access Layer (MAC). In this article we study about the de-authentication DoS (De-DoS) attack in 802.11 Wi-Fi networks. In De-DoS attack an intruder transmits huge spoofed de-authentication frames to the client(s) which is caused their disconnection. All existing methods to overcome from this De-DoS attack are depends upon protocol alterations, encryption, 802.11 standard updating, hardware and software upgrades which are costly. In this article we proposed a novel Machine Learning (ML) based Intrusion Detection System (IDS) to recognize the De-DoS attack in Wi-Fi network which doesn’t suffer from the above weaknesses. We have utilized number of Machine Learning based classifiers for recognition of De-DoS attack. This facilitates an administrator to decide between wide ranges of classification algorithms. The experiments performed using an in-house test bed shows that the proposed ML based IDS discovers De-DoS attack with precision and recall exceeding 96% mark.

Keywords: De-authentication, DoS, Intrusion Detection System, Machine Learning, Wi-Fi Security, WLAN, 802.11

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ISSN (Paper)2224-610X ISSN (Online)2225-0603

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