Reinforcement Learning Assisted Impersonation Attack Detection in Device-to-Device Communications

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-b...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-02, Vol.70 (2), p.1474-1479
Main Authors: Tu, Shanshan, Waqas, Muhammad, Rehman, Sadaqat Ur, Mir, Talha, Abbas, Ghulam, Abbas, Ziaul Haq, Halim, Zahid, Ahmad, Iftekhar
Format: Article
Language:English
Subjects:
Channel estimation
Device-to-device communication
False alarms
impersonation attack
Information technology
Learning
Q-learning
Receivers
secret key generation
Security
Training
Transmitters
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Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3053015