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A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning
Joint Authors
Feng, Yongxin
Zhao, Yuntao
Bo, Bo
Xu, ChunYu
Yu, Bo
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
With explosive growth of malware, Internet users face enormous threats from Cyberspace, known as “fifth dimensional space.” Meanwhile, the continuous sophisticated metamorphism of malware such as polymorphism and obfuscation makes it more difficult to detect malicious behavior.
In the paper, based on the dynamic feature analysis of malware, a novel feature extraction method of hybrid gram (H-gram) with cross entropy of continuous overlapping subsequences is proposed, which implements semantic segmentation of a sequence of API calls or instructions.
The experimental results show the H-gram method can distinguish malicious behaviors and is more effective than the fixed-length n-gram in all four performance indexes of the classification algorithms such as ID3, Random Forest, AdboostM1, and Bagging.
American Psychological Association (APA)
Zhao, Yuntao& Bo, Bo& Feng, Yongxin& Xu, ChunYu& Yu, Bo. 2019. A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1210334
Modern Language Association (MLA)
Zhao, Yuntao…[et al.]. A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning. Security and Communication Networks No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1210334
American Medical Association (AMA)
Zhao, Yuntao& Bo, Bo& Feng, Yongxin& Xu, ChunYu& Yu, Bo. A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1210334
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210334