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Malicious code detection based on CNNs and multi-objective algorithm

文献类型：期刊论文

作者Cui, Zhihua1; Du, Lei1; Wang, Penghong1; Cai, Xingjuan1; Zhang, Wensheng2
刊名JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
出版日期2019-07-01
卷号129页码:50-58
关键词Malicious code Deep learning CNN Imbalance data NSGA-II
ISSN号0743-7315
DOI10.1016/j.jpdc.2019.03.010
通讯作者Cai, Xingjuan(xingjuancai@163.com)
英文摘要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.
WOS关键词GENETIC ALGORITHM ; NEURAL-NETWORKS ; CLASSIFICATION ; OPTIMIZATION
资助项目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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000468255800004
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/24197]  
专题精密感知与控制研究中心_人工智能与机器学习
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.

入库方式： OAI收割

来源：自动化研究所

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