Primary user adversarial attacks on deep learning-based spectrum sensing and the defense method

Abstract

The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we propose primary user adversarial attack (PUAA) to verify the robustness of the deep learning based spectrum sensing model. PUAA adds a carefully manufactured perturbation to the benign primary user signal, which greatly reduces the probability of detection of the spectrum sensing model. We design three PUAA methods in black box scenario. In order to defend against PUAA, we propose a defense method based on autoencoder named DeepFilter. We apply the long short-term memory network and the convolutional neural network together to DeepFilter, so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense. Extensive experiments are conducted to evaluate the attack effect of the designed PUAA method and the defense effect of DeepFilter. Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model. In addition, the experimental results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA without affecting the detection performance of the model.