Malware detection method based on enhanced code images

被引：0
|
作者：
Sun B. [1 ]
Zhang P. [1 ]
Cheng M. [2 ]
Li X. [2 ]
Li Q. [2 ]
机构：
[1] China Information Technology Security Evaluation Center, Beijing
[2] School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2020年 / 60卷 / 05期
关键词：
Code image; Computer virus and prevention; Convolution neural network; Malware; Spatial pyramid pooling;
D O I：
10.16511/j.cnki.qhdxxb.2020.25.008
中图分类号：
学科分类号：
摘要：
Cyberspace malware is becoming more and more serious with traditional malware detection methods unable to deal with the new types of malware. This paper presents a malware detection method based on enhanced code images. The traditional malware image method is improved by using ASCII character information and PE structure information. A three-dimensional RGB image is used as the raw input into the detection algorithm with a VGG16 neural network model with spatial pyramid pooling used to train and predict the malware images. In addition, a multi-label normalized representation method is used to improve the sample label reliability. The method was evaluated against real malware datasets. © 2020, Tsinghua University Press. All right reserved.
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页码：386 / 392
页数：6
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