Detection of De-authentication DoS attacks in Wi-Fi Networks: A Machine Learning Approach

Media Access Layer (MAC) vulnerabilities are the primary reason for the existence of the significant number of Denial of Service (DoS) attacks in 802.11 Wi-Fi networks. In this paper we focus on the de-authentication DoS (Deauth-DoS) attack in Wi-Fi networks. In Deauth-DoS attack an attacker sends a large number of spoofed de-authentication frames to the client(s) resulting in their disconnection. Existing solutions to mitigate Deauth-DoS attack rely on encryption, protocol modifications, 802.11 standard upgradation, software and hardware upgrades which are costly. In this paper we propose a Machine Learning (ML) based Intrusion Detection System (IDS) to detect the Deauth-DoS attack in Wi-Fi network which does not suffer from these drawbacks. To the best of our knowledge ML based techniques have never been used for detection of Deauth-DoS attack. We have used a variety of ML based classifiers for detection of Deauth-DoS attack enabling an administrator to choose among a host of classification algorithms. Experiments performed on in-house test bed shows that the proposed ML based IDS detects Deauth-DoS attack with precision (accuracy) and recall (detection rate) exceeding 96% mark.