| 한글제목(Korean Title) |
A Cross-Platform Malware Variant Classification based on Image Representation |
| 영문제목(English Title) |
A Cross-Platform Malware Variant Classification based on Image Representation |
| 저자(Author) |
Hamad Naeem
Bing Guo
Farhan Ullah
Muhammad Rashid Naeem
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| 원문수록처(Citation) |
VOL 13 NO. 07 PP. 3756 ~ 3777 (2019. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.
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| 키워드(Keyword) |
CSGM
Internet security
Grayscale image
CP-MVCS
Malware Detection
Machine Learning
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| 파일첨부 |
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