Elusive adversarial attacks on machine learning-based hardware fingerprint authentication in optical networks

Abstract

For the physical-layer security enhancement in optical networks, hardware fingerprint authentication (HFA) is proposed by identifying subtle inherent impairments of different optical transmitters in the received signals using neural networks (NNs). However, due to NN’s black-box nature, HFA may be susceptible to adversarial attacks, which is often overlooked in related research, but can lead to HFA erroneously granting high-level permissions to attackers. In this paper, we experimentally demonstrate that classical attacks against NNs, such as projected gradient descent (PGD), are also effective against HFA in optical networks, achieving an average targeted attack success rate of 66.43%. Furthermore, we propose an NN-based white-box attack framework named attack neural network (ATKNN) and a conditional generative adversarial network (CGAN)-based black-box attack framework named attack generator (ATKG). Both ATKNN and ATKG achieve a much higher success rate of ∼ 100%, while their perturbations on the signals are significantly smaller and smoother, making the attacks more elusive. Our work reveals the shortcomings of the mainstream HFA frameworks and lays the foundation for HFA’s future improvements.