Learning-Based Secret Key Generation in Relay Channels Under Adversarial Attacks

Wireless secret key generation (WSKG) facilitates efficient key agreement protocols for securing the sixth generation (6G) wireless networks thanks to its inherently lightweight functionality. Nevertheless, with the existence of adversarial attacks or internal impairments, WSKG can be negatively affected during the randomness distillation, where the legitimate parties measure a source of common randomness. In this article, we propose a
learning-aided
approach for cooperative WSKG under man-in-the-middle (MitM) adversarial attack, while the legitimate nodes suffer from
hardware impairments
(HIs). The key idea is to process the PHY-attribute data on the application layer via deploying a deep neural network (DNN) to enhance the randomness distillation. This way, we realize a learning-based software-centric security solution. More specifically, we take into account the sequence-type nature of observed data, and propose a DNN comprised of
gated recurrent units
(GRUs) to learn the sequence of observations at legitimate endpoints, while the MitM is also alleviated. Our numerical results verify the performance gain of the proposed learning-based approach compared with the state-of-the-arts. Moreover, time and computation complexity of different learning-based models are studied to address the complexity-performance trade-off. Our tests highlight a performance gain of about 43% in terms of mean-square error (MSE) in comparison with a conventional PHY-only scheme.