Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 359-367.doi: 10.11896/jsjkx.221000155

• Information Security • Previous Articles     Next Articles

Generate Transferable Adversarial Network Traffic Using Reversible Adversarial Padding

YANG Youhuan1,2, SUN Lei2, DAI Leyu2, GUO Song2, MAO Xiuqing2, WANG Xiaoqin2   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450000,China
    2 School of Cryptography Engineering,Information Engineering University,Zhengzhou 450001,China
  • Received:2022-10-10 Revised:2023-03-03 Online:2023-12-15 Published:2023-12-07
  • About author:YANG Youhuan,born in 1998,postgraduate.His main research interests include deep learning and adversarial attack/defense.
    SUN Lei,born in 1973,Ph.D,professor.His in research interests include artificial intelligence and information systems security.

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Abstract: More and more deep learning methods are used for network traffic classification,at the same time,it also brings the threat of adversarial network traffic(ANT).ANT will make network traffic classifier based on deep learning method predict incorrectly,and then cause the security protection system to make wrong decision.Although the adversarial algorithms in the vision field can be used to generate ANT,the perturbations generated by these algorithms will change the header information of the network traffic,causing the network traffic to lose its attributes and information.In this paper,the differences of adversarial examples between network traffic tasks and vision tasks are analyzed,and an attack algorithm suitable for generating ANT is proposed,i.e.,reversible adversarial padding(RAP).RAP uses the difference between the length of the network traffic packet and the input length of the network traffic classifier to fill the tail padding area with no -ball perturbations.Besides,to solve the pro-blem that it is difficult to compare the effects of different lengths perturbations,this paper proposes gain on evaluating metrics,which comprehensively considers the impact of the length of the perturbations and the strength of the adversarial attack algorithm.Experimental results show that RAP not only retains the property of network traffic transferability but also obtains a higher gain of attack than traditional algorithms.

Key words: Deep learning, Netwok traffic, Adversarial attack

CLC Number: 

  • TP181
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