An ADS‐B signal poisoning method based on generative adversarial network. Issue 2 (14th January 2023) - Wu, Tianhao; Zhang, Shunjie; Yang, Jungang; Lei, Pengfei
Abstract: Automatic dependent surveillance‐broadcast (ADS‐B) has been widely used due to its low cost and high precision. The deep learning methods for ADS‐B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. An ADS‐B signal poisoning method based on Generative Adversarial Network is proposed. This method can generate poisoned signals. One of ADS‐B signal classification networks is assigned as the attacked network and another one as the protected network. When poisoned signals are fed into these two well‐performed classification networks, the poisoned signal will be recognized incorrectly by the attacked network while classified correctly by the protected network. An attack‐protect‐similar loss function is further proposed to achieve 'triple‐win' in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show that the attacked network classifies poisoned signals with 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%. Abstract : In this letter, we propose an ADS‐B signal poisoning method based on Generative Adversarial Network. This method includes a generator, two discriminators and an Attack‐Protect‐Similar loss. Experimental results show poisoned signals can achieve 'triple‐win' in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals.