Reinforcement learning assisted impersonation attack detection in device-to-device communications

Authors

Shanshan Tu
Muhammad Waqas
Sadaqat Ur Rehman
Talha Mir
Ghulam Abbas
Ziaul Haq Abbas
Zahid Halim
Iftekhar Ahmad, Edith Cowan UniversityFollow

Document Type

Journal Article

Publication Title

IEEE Transactions on Vehicular Technology

Publisher

IEEE

School

Centre for Communications and Electronics Research / School of Engineering

RAS ID

39631

Comments

Tu, S., Waqas, M., Rehman, S. U., Mir, T., Abbas, G., Abbas, Z. H., ... Ahmad, I. (2021). Reinforcement learning assisted impersonation attack detection in device-to-device communications. IEEE Transactions on Vehicular Technology, 70(2), 1474-1479. https://doi.org/10.1109/TVT.2021.3053015

Abstract

IEEE In device-to-device (D2D) communications, the channel gain between a transmitter and a receiver is difficult to predict due to channel variations. Hence, an attacker can easily perform an impersonation attack between two authentic D2D users. As a countermeasure, we propose a reinforcement learning-based technique that guarantees identification of the impersonator based on channel gains. To show the merit of our technique, we report its performance in terms of false alarm rate, miss-detection rate, and average error rate. The secret key generation rate is also determined under the impersonation attack based on physical layer security.

DOI

10.1109/TVT.2021.3053015

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