Vision-Based Malware Detection and Classification Using Lightweight Deep Learning Paradigm-Reference-Cited by-同舟云学术

Vision-Based Malware Detection and Classification Using Lightweight Deep Learning Paradigm

Published:2020 Issue: Volume: Page:62-73
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Abijah Roseline S.,Hari G.,Geetha S.,Krishnamurthy R.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-4018-9_6

Reference19 articles.

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2. Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.S.: Malware images: Visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security, p. 4. ACM (2011)

3. Kolter, J.Z., Maloof, M.A.: Learning to detect and classify malicious executables in the wild. J. Mach. Learn. Res. 7, 2721–2744 (2006)

4. Rieck, K., Trinius, P., Willems, C., Holz, T.: Automatic analysis of malware behavior using machine learning. J. Comput. Secur. 19(4), 639–668 (2011). https://doi.org/10.3233/jcs-2010-0410

5. Islam, R., Tian, R., Batten, L., Versteeg, S.: Classification of malware based on integrated static and dynamic features. J. Netw. Comput. Appl. 36(2), 646–656 (2013). https://doi.org/10.1016/j.jnca.2012.10.004

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