Browse > Article

Analysis of Research Trend on Machine Learning Based Malware Mutant Identification  

Yu, JungBeen (연세대학교 정보보호연구실)
Shin, MinSik (연세대학교 정보보호연구실)
Kwon, Taekyoung (연세대학교 컴퓨터과학과)
Keywords
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Bailey, J. Oberheide, J. Andersen, Z. M. Mao, F. Jahanian, and J. Nazario. Automated classification and analysis of internet malware. In Proc. International Workshop on Recent Advances in Intrusion Detection (RAID), pages 178-197, 2007.
2 U. Bayer, P. M. Comparetti, C. Hlauschek, C. Kruegel, and E. Kirda. Scalable, behavior-based malware clustering. In Proc. Network and Distributed System Security Symposium (NDSS), volume 9, pages 8-11, 2009.
3 K. Rieck, P. Trinius, C. Willems, and T. Holz. Automatic Analysis of Malware Behavior Using Machine Learning. Journal of Computer and Security, 19(4):639-668, 2011.   DOI
4 A. Mohaisen, O. Alrawi, and M. Mohaisen. Amal: High-fidelity, behavior-based automated malware analysis and classification. Journal of Computers and Security, 2015.
5 T. Y. Wang, S. J. Horng, M. Y. Su, C. H. Wu, P. C. Wang, and W. Z. Su. A surveillance spyware detection system based on data mining methods. In Proc. IEEE Congress on Evolutionary Computation, pages 3236-3241, 2006.
6 B. Anderson, C. Storlie, and T. Lane. Improving malware classification: bridging the static/dynamic gap. In Proc. Artificial Intelligence and Security (AISec), pages 3-14, 2012.
7 M. Eskandari, Z. Khorshidpour, and S. Hashemi. Hdm-analyser: a hybrid analysis approach based on data mining techniques for malware detection. Journal of Computer Virology and Hacking Techniques, 9(2):77-93, 2013.   DOI
8 R. Islam, R. Tian, L. M. Batten, and S. Versteeg. Classification of malware based on integrated static and dynamic features. Journal of Network and Computer Applications, 36(2): 646-656, 2013.   DOI
9 https://www.av-test.org/en
10 M. G. Schultz, E. Eskin, F. Zadok, and S. J. Stolfo. Data mining methods for detection of new malicious executables. In Proc. IEEE Symposium on Security and Privacy (S&P), pages 38-49, 2001.
11 J. Z. Kolter, and M. A. Maloof. Learning to detect and classify malicious executables in the wild. Journal of Machine Learning Research, pages 2721-2744, 2006.
12 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), pages 183-194, 2016.
13 M. E. Karim, A. Walenstein, A. Lakhotia, and L. Parida. Malware phylogeny generation using permutations of code. Journal of Computer Virology, 1(1-2):13-23, 2005.   DOI
14 X. Hu, K. G. Shin, S. Bhatkar, and K. Griffin. MutantX-S: scalable malware clustering based on static features. In Proc. USENIX Conference on Annual Technical Conference (ATC), pages 187-198, 2013.
15 K. Rieck, T. Holz, C. Willems, P. Düssel, and P. Laskov. Learning and classification of malware behavior. In Proc. Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA), pages 108-125, 2008.