Technology of Information Security
|
App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
240-244

Malware classification method based on convolutional neural network and multi-feature fusion

Zheng Jue
Ou Yuyi
School of Computers, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0258

Abstract

In order to reduce the impact of pack and obfuscation on malware classification and improve the accuracy, this paper proposed a malware classification method based on convolutional neural network and multi-feature fusion. The classifier was based on convolutional neural network and it took grayscale image of malware and the mixed sequence with API function call and opcode as features. The classifier had three components: image component, sequence component and fusion component. After training, the classifier could detect malware categories. The experimental results show that this method has higher classification accuracy and macro-F1 than some existing methods such as HYDRA and Orthrus. This method can classify malware more accurately and reduce the impact of packing and obfuscation.

Foundation Support

广州市科技计划资助项目(201902020007,202007010004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0258
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Technology of Information Security
Pages: 240-244
Serial Number: 1001-3695(2022)01-042-0240-05

Publish History

[2021-10-12] Accepted Paper
[2022-01-05] Printed Article

Cite This Article

郑珏, 欧毓毅. 基于卷积神经网络与多特征融合恶意代码分类方法 [J]. 计算机应用研究, 2022, 39 (1): 240-244. (Zheng Jue, Ou Yuyi. Malware classification method based on convolutional neural network and multi-feature fusion [J]. Application Research of Computers, 2022, 39 (1): 240-244. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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