Open Access
| Issue |
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 5 | |
| Section | Session 2: Signal and Image Processing | |
| DOI | https://doi.org/10.1051/itmconf/20160702001 | |
| Published online | 21 November 2016 | |
- Rad B B, Masrom M, Ibrahim S. Camouflage in malware: from encryption to metamorphism[J]. International Journal of Computer Science and Network Security, 2012, 12(8): 74–83. [Google Scholar]
- Moser A, Kruegel C, Kirda E. Limits of static analysis for malware detection[C]//Computer security applications conference, 2007. ACSAC 2007. Twenty-third annual. IEEE, 2007: 421–430. [Google Scholar]
- Willems C, Holz T, Freiling F. Toward automated dynamic malware analysis using cwsandbox[J]. IEEE Security & Privacy, 2007 (2): 32–39. [CrossRef] [Google Scholar]
- Egele M, Scholte T, Kirda E, et al. A survey on automated dynamic malware-analysis techniques and tools[J]. ACM Computing Surveys (CSUR), 2012, 44(2): 6. [CrossRef] [Google Scholar]
- Bayer U, Comparetti P M, Hlauschek C, et al. Scalable, Behavior-Based Malware Clustering[C]//NDSS. 2009, 9: 8–11. [Google Scholar]
- Forrest S, Hofmeyr S, Somayaji A. The evolution of system-call monitoring[C]//Computer Security Applications Conference, 2008. ACSAC 2008. Annual. IEEE, 2008: 418–430. [Google Scholar]
- Irwin G W, Warwick K, Hunt K J. Neural network applications in control[M]. Iet, 1995. [CrossRef] [Google Scholar]
- Rieck K, Holz T, Willems C, et al. Learning and classification of malware behavior[M]//Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Berlin Heidelberg, 2008: 108–125. [CrossRef] [Google Scholar]
- Shaid M, Zainudeen S, Maarof M A. Malware behavior image for malware variant identification[C]//Biometrics and Security Technologies (ISBAST), 2014 International Symposium on. IEEE, 2014: 238–243. [Google Scholar]
- Liang G, Pang J, Dai C. A Behavior-Based Malware Variant Classification Technique[J]. International Journal of Information and Education Technology, 2016, 6(4): 291. [CrossRef] [Google Scholar]
- Park Y, Reeves D, Mulukutla V, et al. Fast malware classification by automated behavioral graph matching[C]//Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research. ACM, 2010: 45. [Google Scholar]
- Cesare S, Xiang Y. Classification of malware using structured control flow[C]//Proceedings of the Eighth Australasian Symposium on Parallel and Distributed Computing-Volume 107. Australian Computer Society, Inc., 2010: 61–70. [Google Scholar]
- Tian R, Batten L M, Versteeg S C. Function length as a tool for malware classification[C]//Malicious and Unwanted Software, 2008. MALWARE 2008. 3rd International Conference on. IEEE, 2008: 69–76. [CrossRef] [Google Scholar]
- Islam R, Tian R, Batten L, et al. Classification of malware based on string and function feature selection[C] // Cybercrime and Trustworthy Computing Workshop (CTC), 2010 Second. IEEE, 2010: 9–17. [Google Scholar]
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