A Study on Variant Malware Detection Techniques Using Static and Dynamic Features


Jinsu Kang, Yoojae Won, Journal of Information Processing Systems Vol. 16, No. 4, pp. 882-895, Aug. 2020  

10.3745/JIPS.03.0145
Keywords: computer security, Dynamic Analysis Machine Learning, Metamorphic, Polymorphic, Static Analysis, Windows Malware
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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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Cite this article
[APA Style]
Jinsu Kang and Yoojae Won (2020). A Study on Variant Malware Detection Techniques Using Static and Dynamic Features. Journal of Information Processing Systems, 16(4), 882-895. DOI: 10.3745/JIPS.03.0145.

[IEEE Style]
J. Kang and Y. Won, "A Study on Variant Malware Detection Techniques Using Static and Dynamic Features," Journal of Information Processing Systems, vol. 16, no. 4, pp. 882-895, 2020. DOI: 10.3745/JIPS.03.0145.

[ACM Style]
Jinsu Kang and Yoojae Won. 2020. A Study on Variant Malware Detection Techniques Using Static and Dynamic Features. Journal of Information Processing Systems, 16, 4, (2020), 882-895. DOI: 10.3745/JIPS.03.0145.