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A Co-evolutionary Algorithm-Based Malware Adversarial Sample Generation Method | IEEE Conference Publication | IEEE Xplore

A Co-evolutionary Algorithm-Based Malware Adversarial Sample Generation Method


Abstract:

The study of adversarial attacks on malicious code detection models will help identify and improve the flaws of detection models, improve the detection ability of adversa...Show More

Abstract:

The study of adversarial attacks on malicious code detection models will help identify and improve the flaws of detection models, improve the detection ability of adversarial attacks, and enhance the security of AI (Artificial Intelligent) algorithm-based applications. To address the problems of low efficiency, long time, and low evasion rate in generating adversarial samples, we propose a co-evolutionary algorithm-based adversarial sample generation method. We decompose the adversarial sample generation problem into three sub-problems, which are minimizing the number of modification actions, injecting less content, and being detected as benign by the target model. The two sub-problems of injecting less content and being detected as benign by the target model can be completed by minimizing the fitness function through the cooperation of two populations in coevolution. Minimizing the number of actions is achieved by a selection operation in the evolutionary process. We perform attack experiments on static malicious detection models and commercial detection engines. The experimental results show the generated adversarial samples can improve the evasion rate of some detection engines while ensuring the minimum number of modification actions and injecting less content. On the two static malicious detection models, our approach achieves more than an 80% evasion rate with fewer modification actions and injected content. The evasion rate on three commercial detection engines can reach 58.9%. Uploading the generated adversarial samples to the VirusTotal platform can evade an average of 54.0% of the anti-virus programs on the platform. Our approach is also compared with the adversarial attack approach based on an evolutionary algorithm to verify the necessity of minimizing the number of modification actions and injecting less content in adversarial sample generation.
Date of Conference: 22-24 June 2022
Date Added to IEEE Xplore: 26 September 2022
ISBN Information:
Conference Location: Edinburgh, United Kingdom

Funding Agency:


I. Introduction

Recently, machine learning-based malware detection approaches have been extensively applied to cyberspace security and achieved excellent detection outcomes [1]–[3]. However, since machine learning models are designed at the early stage only considering the implementation of functions without considering their security issues, they have apparent vulnerabilities in defending against adversarial attacks [4]–[7]. There are not effective methods to defend against adversarial attacks, especially in the field of malware adversary [8]–[13]. Therefore, understanding the generation principle of adversarial samples will provide an essential theoretical basis and technical support for future adversarial defense research.

References

References is not available for this document.