RSS-based spoofing detection and localization algorithm in IEEE 802.11 wireless networks

Wireless networks have gained immense popularity in recent years. Though wireless networks have innumerous advantages over conventional wired networks, the borderless nature of wireless networks makes it prone to various felonious activities. In the context of network security, one of the most cruci...

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Bibliographic Details
Published in:	2016 International Conference on Communication and Signal Processing (ICCSP) pp. 1642 - 1645
Main Authors:	Gajbhiye, Yamini, Daruwala, R. D.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2016
Subjects:	Algorithm design and analysis Clustering algorithms DANN Fingerprint recognition IEEE 802.11 Standard Localization Mean Shift Algorithm Received Signal Strength (RSS) Spoofing Attacks Wireless networks Wireless sensor networks
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Summary:	Wireless networks have gained immense popularity in recent years. Though wireless networks have innumerous advantages over conventional wired networks, the borderless nature of wireless networks makes it prone to various felonious activities. In the context of network security, one of the most crucial vulnerabilities which have caused a serious global concern is the MAC Address Spoofing. MAC address spoofing facilitates launching of other attacks such as Denial-of-service, Man-in-the-middle, SYN flooding etc. In this paper we propose a methodology for the detection of spoofing attacks in IEEE 802.11 networks that involve performance of cluster analysis on RSS patterns of 802.11 transmitters which not only detect presence of a spoofing attack but also determine number of attackers. Further for localizing the adversaries, we propose a discriminant-adaptive neural network (DANN) based localization system.
DOI:	10.1109/ICCSP.2016.7754440