Automatic malware classification and new malware detection using machine learning-Reference-Cited by-同舟云学术

Automatic malware classification and new malware detection using machine learning

Published:2017-09 Issue:9 Volume:18 Page:1336-1347
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Frontiers Inf Technol Electronic Eng

Author:

Liu Liu^ORCID,Wang Bao-sheng,Yu Bo,Zhong Qiu-xi

Funder

National Natural Science Foundation of China

the National Basic Research Program (973) of China

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

http://link.springer.com/article/10.1631/FITEE.1601325/fulltext.html

Reference41 articles.

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2. Cheng, J.Y.C., Tsai, T.S., Yang, C.S., 2013. An information retrieval approach for malware classification based on Windows API calls. Int. Conf. on Machine Learning and Cybernetics, p.1678–1683. https://doi.org/10.1109/ICMLC.2013.6890868

3. Damodaran, A., di Troia, F., Visaggio, C.A., et al., 2017. A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hack. Tech., 13(1): 1–12. https://doi.org/10.1007/s11416-015-0261-z

4. Ding, Y.X., Dai, W., Yan, S.L., et al., 2014. Control flowbased Opcode behavior analysis for malware detection. Comput. Secur., 44:65–74. https://doi.org/10.1016/j.cose.2014.04.003

5. Egele, M., Scholte, T., Kirda, E., et al., 2012. A survey on automated dynamic malware-analysis techniques and tools. ACM Comput. Surv., 44(2): Article 6. https://doi.org/10.1145/2089125.2089126

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