DocumentCode :
3727948
Title :
Detection of De-Authentication DoS Attacks in Wi-Fi Networks: A Machine Learning Approach
Author :
Mayank Agarwal;Santosh Biswas;Sukumar Nandi
Author_Institution :
Dept. of Comput. Sci. &
fYear :
2015
Firstpage :
246
Lastpage :
251
Abstract :
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 up gradation, 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.
Keywords :
"IEEE 802.11 Standard","Computer crime","Encryption","Authentication","Protocols","Software"
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/SMC.2015.55
Filename :
7379187
Link To Document :
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