Computer Science ›› 2023, Vol. 50 ›› Issue (7): 325-331.doi: 10.11896/jsjkx.220800176

• Information Security • Previous Articles     Next Articles

Adversarial Malware Generation Method Based on Genetic Algorithm

LI Kun1, GUO Wei1, ZHANG Fan1, DU Jiayu2, YANG Meiyue2   

  1. 1 Institute of Information Technology,University of Information Engineering,Zhengzhou 450002,China
    2 Purple Mountain Laboratories,Nanjing 211111,China
  • Received:2022-08-17 Revised:2022-11-26 Online:2023-07-15 Published:2023-07-05
  • About author:LI Kun,born in 1998,postgraduate.His main research interests include artificial intelligence security,adversarial samples,and malware detection.ZHANG Fan,born in 1981,Ph.D,associate researcher,master tutor.His main research interests include active defense,chip design technology,and high-performance computing.

Abstract: In recent years,with the development of Internet technology,malware has become an important method of network attack.To defend against malware attacks,deep learning techniques can be applied to malware detection.However,due to the limitations of deep learning models,malware detection models based on deep learning are vulnerable to adversarial malware,which leads to adversarial malware evading model detection.By studying the generation of adversarial malware,it can help modeldesig-ners to improve model design,improve model robustness and defense capabilities.Therefore,for the malware detection model based on grayscale image,the adversarial malware generation method based on genetic algorithm is proposed.It optimizes the perturbation by genetic algorithm,and then injects the perturbation into the malware by the obfuscation operation,so as to ensure that the generated adversarial malware samples are adversarial,executable and malicious.It is verified by experiments that the attack success rate of adversarial samples generated by the proposed method increases by 56.4% on average compared to the exis-ting work.

Key words: Adversarial examples, Deep learning, Malware detection, Adversarial attacks, Genetic algorithms

CLC Number: 

  • TP309.5
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