Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion

Chong, Xulei; Gao, Yating; Zhang, Ru; Liu, Jianyi; Huang, Xingjie; Zhao, Jinmeng

doi:10.3390/electronics11193064

Open AccessArticle

Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion

by

Xulei Chong

¹,

Yating Gao

²,

Ru Zhang

^1,*,

Jianyi Liu

¹,

Xingjie Huang

² and

Jinmeng Zhao

²

¹

School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China

²

State Grid Information & Telecommunication Branch, Beijing 100761, China

^*

Author to whom correspondence should be addressed.

Electronics 2022, 11(19), 3064; https://doi.org/10.3390/electronics11193064

Submission received: 20 August 2022 / Revised: 14 September 2022 / Accepted: 22 September 2022 / Published: 26 September 2022

(This article belongs to the Special Issue Advances in Complex Cyberattack Detection)

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Abstract

A malware family classification method based on Efficient-Net and 1D-CNN fusion is proposed. Given the problem that some local information of malware itself as one-dimensional data will be lost when the malware is imaged, the malware is converted into an image and one-dimensional vector and then input into two neural networks. The network of two-dimensional convolution architecture is used to extract the texture features of malware, and the one-dimensional convolution is used to extract the features of local adjacent information, the deep characteristics of different networks are fused, and the two networks are modified at the same time during backpropagation. This method not only extracts the texture features of malware but also saves the features of the malware itself as one-dimensional data, which shows better performance for multiple datasets.

Keywords: deep learning; malware family classification; image classification; feature fusion

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MDPI and ACS Style

Chong, X.; Gao, Y.; Zhang, R.; Liu, J.; Huang, X.; Zhao, J. Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion. Electronics 2022, 11, 3064. https://doi.org/10.3390/electronics11193064

AMA Style

Chong X, Gao Y, Zhang R, Liu J, Huang X, Zhao J. Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion. Electronics. 2022; 11(19):3064. https://doi.org/10.3390/electronics11193064

Chicago/Turabian Style

Chong, Xulei, Yating Gao, Ru Zhang, Jianyi Liu, Xingjie Huang, and Jinmeng Zhao. 2022. "Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion" Electronics 11, no. 19: 3064. https://doi.org/10.3390/electronics11193064

APA Style

Chong, X., Gao, Y., Zhang, R., Liu, J., Huang, X., & Zhao, J. (2022). Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion. Electronics, 11(19), 3064. https://doi.org/10.3390/electronics11193064

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Classification of Malware Families Based on Efficient-Net and 1D-CNN Fusion

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