Malicious code detection based on CNNs and multi-objective algorithm

doi:10.1016/j.jpdc.2019.03.010

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Malicious code detection based on CNNs and multi-objective algorithm
	Cui, Zhihua 1; Du, Lei 1; Wang, Penghong 1; Cai, Xingjuan 1; Zhang, Wensheng2
发表期刊	JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
ISSN	0743-7315
	2019-07-01
卷号	129 页码:50-58
通讯作者	Cai, Xingjuan(xingjuancai@163.com)
摘要	An increasing amount of malicious code causes harm on the internet by threatening user privacy as one of the primary sources of network security vulnerabilities. The detection of malicious code is becoming increasingly crucial, and current methods of detection require much improvement. This paper proposes a method to advance the detection of malicious code using convolutional neural networks (CNNs) and intelligence algorithm. The CNNs are used to identify and classify grayscale images converted from executable files of malicious code. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then employed to deal with the data imbalance of malware families. A series of experiments are designed for malware image data from Vision Research Lab. The experimental results demonstrate that the proposed method is effective, maintaining higher accuracy and less loss. (C) 2019 Elsevier Inc. All rights reserved.
关键词	Malicious code Deep learning CNN Imbalance data NSGA-II
DOI	10.1016/j.jpdc.2019.03.010
关键词[WOS]	GENETIC ALGORITHM ; NEURAL-NETWORKS ; CLASSIFICATION ; OPTIMIZATION
收录类别	SCI
语种	英语
资助项目	PhD Research Startup Foundation of Taiyuan University of Science and Technology, China[20182002] ; Scientific and Technological innovation Team of Shanxi Province, China[201805D131007] ; Natural Science Foundation of Shanxi Province, China[201801D121127] ; National Natural Science Foundation of China[61663028] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61663028] ; Natural Science Foundation of Shanxi Province, China[201801D121127] ; Scientific and Technological innovation Team of Shanxi Province, China[201805D131007] ; PhD Research Startup Foundation of Taiyuan University of Science and Technology, China[20182002]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province, China ; Scientific and Technological innovation Team of Shanxi Province, China ; PhD Research Startup Foundation of Taiyuan University of Science and Technology, China
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:000468255800004
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
引用统计	被引频次：129[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/24198
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Cai, Xingjuan
作者单位	1.TaiYuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cui, Zhihua,Du, Lei,Wang, Penghong,et al. Malicious code detection based on CNNs and multi-objective algorithm[J]. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING,2019,129:50-58.
APA	Cui, Zhihua,Du, Lei,Wang, Penghong,Cai, Xingjuan,&Zhang, Wensheng.(2019).Malicious code detection based on CNNs and multi-objective algorithm.JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING,129,50-58.
MLA	Cui, Zhihua,et al."Malicious code detection based on CNNs and multi-objective algorithm".JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 129(2019):50-58.