闫佳,
聂楚江,
苏璞睿,
1.中国科学院大学计算机科学与技术学院 北京 100190
2.中国科学院软件研究所可信计算与信息保障实验室 北京 100190
基金项目:国家自然科学基金(61902384, U1836117, U1836113)
详细信息
作者简介:闫佳：男，1991年生，博士生，研究方向为网络与系统安全
闫佳：男，1986年生，副研究员，研究方向为网络与系统安全
聂楚江：男，1983年生，副研究员，研究方向为网络与系统安全
苏璞睿：男，1976年生，研究员，研究方向为网络与系统安全
通讯作者:苏璞睿 purui@iscas.ac.cn
中图分类号:TP309.5计量
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被引次数:0
出版历程
收稿日期:2019-12-31
修回日期:2020-05-30
网络出版日期:2020-07-21
刊出日期:2020-09-27
Method for Generating Malicious Code Adversarial Samples Based on Genetic Algorithm
Jia YAN,Jia YAN,
Chujiang NIE,
Purui SU,
1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China
2. Trusted Computing and Information Assurance Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Funds:The National Natural Science Foundation of China (61902384, U1836117, U1836113)
摘要
摘要:机器学习已经广泛应用于恶意代码检测中，并在恶意代码检测产品中发挥重要作用。构建针对恶意代码检测机器学习模型的对抗样本，是发掘恶意代码检测模型缺陷，评估和完善恶意代码检测系统的关键。该文提出一种基于遗传算法的恶意代码对抗样本生成方法，生成的样本在有效对抗基于机器学习的恶意代码检测模型的同时，确保了恶意代码样本的可执行和恶意行为的一致性，有效提升了生成对抗样本的真实性和模型对抗评估的准确性。实验表明，该文提出的对抗样本生成方法使MalConv恶意代码检测模型的检测准确率下降了14.65%；并可直接对VirusTotal中4款基于机器学习的恶意代码检测商用引擎形成有效的干扰，其中，Cylance的检测准确率只有53.55%。
关键词:恶意代码检测/
机器学习/
对抗样本
Abstract:Machine learning is widely used in malicious code detection and plays an important role in malicious code detection products. Constructing adversarial samples for malicious code detection machine learning models is the key to discovering defects in malicious code detection models, evaluating and improving malicious code detection systems. This paper proposes a method for generating malicious code adversarial samples based on genetic algorithms. The generated samples combat effectively the malicious code detection model based on machine learning, while ensuring the consistency of the executable and malicious behavior of malicious code samples, and improving effectively the authenticity of the generated adversarial samples and the accuracy of the model adversarial evaluation are presented. The experiments show that the proposed method of generating adversarial samples reduces the detection accuracy of the MalConv malicious code detection model by 14.65%, and can directly interfere with four commercial machine-based malicious code detection engines in VirusTotal. Among them, the accuracy rate of Cylance detection is only 53.55%.
Key words:Malware detection/
Machine learning/
Adversarial sample
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