Colluding RF Fingerprint Impersonation Attack Based on Generative Adversarial Network
Yuxuan Xu, Ming Liu, Linning Peng, Junqing Zhang, Yawen Zheng · 2022
Radio frequency fingerprint (RFF) is an effective way to improve the security of wireless communications. Existing research mainly focused on the classification capability and the robustness of RFFs but overlooked malicious attacks. In this paper, a colluding impersonation attack framework is proposed to emulate the RFF of legitimate users. A colluding attacker is introduced to observe the signal features of the impersonation attacker and the legitimate user and compare their difference. The difference is fed back to the impersonation attacker to help improve its RFF impersonation method. With this idea, the impersonation attack is realized by the Generative Adversarial Network (GAN) structure. The RFF impersonation is formulated as the generator whose objective is to output the signal with RFF similar to the legitimate user, viewed from the colluding attacker’s perspective. Simulation results show that the proposed method can effectively impersonate the legitimate user’s RFF under the dynamic block fading channel.