Controllable Wireless Spoofing Attack Based on Conditional BEGAN and Auxiliary Channel Sensing

This paper investigates how to build a controllable wireless spoofing attack launch framework that is driven by fundamental channel modeling and practical wireless datasets. First, we propose a wireless spoofing attack scheme against the defense mechanism with adversarial deep learning. To obtain channel characteristics and facilitate offline training of the attack model, auxiliary channel sensing is proposed with fundamental channel modeling. Based on these, a conditional boundary equilibrium generative adversarial network (CBEGAN) is designed with adversarial autoencoder (AAE), which takes true labels of signals and channel characteristics as conditions and enables the generation of controllable spoofing signals to fool the protected legitimate classifier. We verify the performance of the proposed spoofing attack scheme with CBEGAN and channel sensing by using wireless datasets, which contain signal data of multiple emitters and modulation types. Results show that the proposed scheme outperforms random attack, replay attack, and the recent attack scheme based on generative adversarial network (GAN) when a single legitimate emitter sends a fixed modulation type. It is also shown that the average attack success probability of the proposed CBEGAN attack model can reach more than 80% while mimicking multiple emitters and modulation types. The performance of the proposed scheme on different channel conditions including signal-to-noise ratio (SNR) and K-factor of the Rician fading channel is evaluated.