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http://dx.doi.org/10.13089/JKIISC.2018.28.5.1161

Development Research of An Efficient Malware Classification System Using Hybrid Features And Machine Learning  

Yu, Jung-Been (Information Security Lab., Graduation School of Information, Yonsei University)
Oh, Sang-Jin (Information Security Lab., Graduation School of Information, Yonsei University)
Park, Leo-Hyun (Information Security Lab., Graduation School of Information, Yonsei University)
Kwon, Tae-Kyoung (Information Security Lab., Graduation School of Information, Yonsei University)
Abstract
In order to cope with dramatically increasing malware variant, malware classification research is getting diversified. Recent research tend to grasp individual limits of existing malware analysis technology (static/dynamic), and to change each method into "hybrid analysis", which is to mix different methods into one. Futhermore, it is applying machine learning to identify malware variant more accurately, which are difficult to classify. However, accuracy and scalability of trade-off problems that occur when using all kinds of methods are not yet to be solved, and it is still an important issue in the field of malware research. Therefore, to supplement and to solve the problems of the original malware classification research, we are focusing on developing a new malware classification system in this research.
Keywords
Malware; Classification; Machine Learning; ssdeep;
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