Reinforcement learning assisted impersonation attack detection in device-to-device communications
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.
RAS ID
39631
Document Type
Journal Article
Date of Publication
2021
School
Centre for Communications and Electronics Research / School of Engineering
Copyright
subscription content
Publisher
IEEE
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