Title: Detection and fine-grained classification of malicious code using convolutional neural networks and swarm intelligence algorithms

Authors: Dongzhi Cao; Xinglan Zhang; Yang Cao; Yuehan Wang; Weixin Liu

Addresses: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China ' Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China ' School of Information, Beijing Wuzi University, Beijing 100149, China ' Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China ' NSFOCUS Information Technology Co., Ltd., Beijing 100089, China

Abstract: With the development of society, network security has received more and more attention. Malicious code has also grown, causing network security vulnerabilities and increasing threats to internet security. Therefore, the detection of malicious code becomes very important. However, there are some problems in the current research on malicious code detection, for example, tedious feature extraction and unbalanced data, which is far from the effect people want to achieve. To address these problems, in this paper, we propose a novel malicious code detection and fine-grained classification model by using convolutional neural networks and swarm intelligence algorithms. We converted the binary executable files of malicious codes into greyscale images and then used convolution neural networks to detect and classify malicious codes. In addition, we employed swarm intelligence algorithms to achieve fine-grained classification on unbalanced data in different malicious code families. We conducted a series of experiments on the real malware dataset from Vision Research Lab. The experimental results demonstrated that the proposed solution is effective for fine-grained classification of malicious codes.

Keywords: malicious code; unbalanced data; fine-grained classification; swarm intelligence algorithms.

DOI: 10.1504/IJWMC.2020.109235

International Journal of Wireless and Mobile Computing, 2020 Vol.19 No.1, pp.1 - 8

Received: 17 Dec 2018
Accepted: 05 Nov 2019
Published online: 02 Sep 2020 *

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