Adversarial Waveform Design for Wireless Transceivers Toward Intelligent Eavesdropping

In wireless communications, the communication channel between the transmitter and receiver can be monitored by an eavesdropper. The eavesdropper uses deep learning (DL) to quickly identify the modulation parameters of signals and further disrupt legitimate communications. Since DL has been proven to be vulnerable to adversarial attacks, this paper proposes to attack the eavesdropper's model by designing adversarial waveforms, preventing the eavesdropper from correctly identifying the modulation schemes used by legitimate users, and thereby preventing the eavesdropper from interfering with normal communications. This paper proposes an attention-based black-box attack method, which uses the prediction of different networks in the ensemble model to assign adversarial attention factors to each network. This greatly improves the transmission attack performance of the designed adversarial examples. In addition, by analysing the influence of the channel on the adversarial waveform, we further design the adversarial waveform that can be transmitted in the channel to improve the practicability of the attack algorithm. Finally, we theoretically derive the bounds of the adversarial risk increase that the attack brings to the target model. Simulation results show that the proposed method can improve the success rate of the attack on the eavesdropper's modulation detection model, cause the model to misidentify the signal modulation type, and improve the security and reliability of legitimate transceivers in wireless communication systems.