Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method  

作　　者：Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen Huaji Zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang 

机构地区：[1]Science and Technology on Communication Information Security Control Laboratory,Jiaxing,314000,China [2]College of Information Engineering,Zhejiang University of Technology,Hangzhou,310012,China [3]Institute of Cyberspace Security,Zhejiang University of Technology,Hangzhou,310012,China [4]School of Communication Engineering,Hangzhou Dianzi University,Hangzhou,310018,China

出　　处：《China Communications》2021年第12期94-107,共14页中国通信（英文版）

基　　金：the National Nat-ural Science Foundation of China under Grant No.62072406,No.U19B2016,No.U20B2038 and No.61871398;the Natural Science Foundation of Zhejiang Province under Grant No.LY19F020025;the Major Special Funding for“Science and Tech-nology Innovation 2025”in Ningbo under Grant No.2018B10063.

摘　　要：The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,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 care-fully 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 or-der 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 eval-uate 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 experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.

关 键 词：spectrum sensing cognitive radio deep learning adversarial attack autoencoder DEFENSE 

分 类 号：TN925[电子电信—通信与信息系统]