JIPS (한국정보처리학회)
Current Result Document : 3 / 6
| 한글제목(Korean Title) |
A Study on Variant Malware Detection Techniques Using Static and Dynamic Features |
| 영문제목(English Title) |
A Study on Variant Malware Detection Techniques Using Static and Dynamic Features |
| 저자(Author) |
Jinsu Kang
Yoojae Won
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| 원문수록처(Citation) |
VOL 16 NO. 04 PP. 0882 ~ 0895 (2020. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.
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| 키워드(Keyword) |
Computer Security
Dynamic Analysis Machine Learning
Metamorphic
Polymorphic
Static Analysis
Windows Malware
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| 파일첨부 |
PDF 다운로드
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