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RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach †

Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada
Author to whom correspondence should be addressed.
This paper is an extension version of the conference paper: Madani, P.; Vlajic, N.; Sadeghpour, S. MAC-Layer Spoofing Detection and Prevention in IoT Systems: Randomized Moving Target Approach. In Proceedings of the 2020 Joint Workshop on CPS & IoT Security and Privacy, Virtual Event, USA, 9 November 2020.
Academic Editors: Phil Legg and Giorgio Giacinto
J. Cybersecur. Priv. 2021, 1(3), 453-469;
Received: 20 May 2021 / Revised: 26 July 2021 / Accepted: 29 July 2021 / Published: 12 August 2021
(This article belongs to the Collection Machine Learning and Data Analytics for Cyber Security)
In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner. View Full-Text
Keywords: IoT security; spoofing; MAC authentication; intrusion detection system; LSTM autoencoders IoT security; spoofing; MAC authentication; intrusion detection system; LSTM autoencoders
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MDPI and ACS Style

Madani, P.; Vlajic, N. RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach. J. Cybersecur. Priv. 2021, 1, 453-469.

AMA Style

Madani P, Vlajic N. RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach. Journal of Cybersecurity and Privacy. 2021; 1(3):453-469.

Chicago/Turabian Style

Madani, Pooria, and Natalija Vlajic. 2021. "RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach" Journal of Cybersecurity and Privacy 1, no. 3: 453-469.

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