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Article

A New MAC Address Spoofing Detection Technique Based on Random Forests

Computer Science and Engineering Department, University of Bridgeport, 126 Park Ave, Bridgeport, CT 06604, USA
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Academic Editor: Leonhard M. Reindl
Sensors 2016, 16(3), 281; https://doi.org/10.3390/s16030281
Received: 21 December 2015 / Revised: 15 February 2016 / Accepted: 19 February 2016 / Published: 24 February 2016
(This article belongs to the Section Sensor Networks)
Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests. View Full-Text
Keywords: MAC address; spoofing; detection; random forests; wireless sensor networks; wireless local area networks MAC address; spoofing; detection; random forests; wireless sensor networks; wireless local area networks
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MDPI and ACS Style

Alotaibi, B.; Elleithy, K. A New MAC Address Spoofing Detection Technique Based on Random Forests. Sensors 2016, 16, 281. https://doi.org/10.3390/s16030281

AMA Style

Alotaibi B, Elleithy K. A New MAC Address Spoofing Detection Technique Based on Random Forests. Sensors. 2016; 16(3):281. https://doi.org/10.3390/s16030281

Chicago/Turabian Style

Alotaibi, Bandar, and Khaled Elleithy. 2016. "A New MAC Address Spoofing Detection Technique Based on Random Forests" Sensors 16, no. 3: 281. https://doi.org/10.3390/s16030281

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