CASIA OpenIR
Malicious code detection based on CNNs and multi-objective algorithm
Cui, Zhihua1; Du, Lei1; Wang, Penghong1; Cai, Xingjuan1; Zhang, Wensheng2
Source PublicationJOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
ISSN0743-7315
2019-07-01
Volume129Pages:50-58
Corresponding AuthorCai, Xingjuan(xingjuancai@163.com)
AbstractAn 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.
KeywordMalicious code Deep learning CNN Imbalance data NSGA-II
DOI10.1016/j.jpdc.2019.03.010
WOS KeywordGENETIC ALGORITHM ; NEURAL-NETWORKS ; CLASSIFICATION ; OPTIMIZATION
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaComputer Science
WOS SubjectComputer Science, Theory & Methods
WOS IDWOS:000468255800004
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24198
Collection中国科学院自动化研究所
Corresponding AuthorCai, Xingjuan
Affiliation1.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
Recommended Citation
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.
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