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Development Research of An Efficient Malware Classification System Using Hybrid Features And Machine Learning
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하이브리드 특징 및 기계학습을 활용한 효율적인 악성코드 분류 시스템 개발 연구

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Type
Academic journal
Author
Jung-Been Yu (연세대학교) Sang-Jin Oh (연세대학교) Leo-Hyun Park (연세대학교) Tae-Kyoung Kwon (연세대학교)
Journal
Korea Institute Of Information Security And Cryptology Journal of the Korea Institute of Information Security & Cryptology Vol.28 No.5 KCI Accredited Journals
Published
2018.10
Pages
1,161 - 1,167 (7page)

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Development Research of An Efficient Malware Classification System Using Hybrid Features And Machine Learning
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Research history (2)

2018

Development Research of An Efficient Malware Classification System Using Hybrid Features And Machine Learning
Jung-Been Yu ,  Sang-Jin Oh ,  Leo-Hyun Pa and others 2 Journal of the Korea Institute of Information Security & Cryptology 2018.10 Academic journal
Development of efficient malware classification system using machine learning
유정빈 2018.01 Thesis
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Abstract· Keywords

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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.
#Malware #Classification #Machine Learning #ssdeep

Contents

요약
ABSTRACT
I. 서론
II. 기반 기술 소개
III. 시스템 설계
IV. 실험 설계 및 결과
V. 관련 연구
VI. 결론
References

References (7)

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AVTEST, “AVTEST Annual Report”, http s://www.av-test.org/en, May. 2018. SSDEEP, “ssdeep - Fuzzy hashing progra m”, https://ssdeep-project.github.io/ssdee p/, May. 2018. VX Heaven, http://83.133.184.251/virensi mulation.org/, May. 2018. Virus Total, “VirusTotal - Free Online Virus, Malware and URL Scanner”, https://www.virustotal.com/ko/, May. 2018. M. Ahmadi, D. Ulyanov, S. Semenov, M. Trofimov, and G. Giacinto. “Novel feature extraction, selection and fusion for effective malware family classification,” In Proc. Data and Application Security and Privacy (CODASPY), pp. 183?194, Mar. 2016

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