Performance Evaluation of CNN and Pre-trained Models for Malware Classification-Reference-Cited by-同舟云学术

Performance Evaluation of CNN and Pre-trained Models for Malware Classification

Published:2023-01-30 Issue:8 Volume:48 Page:10355-10369
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Habibi Omar,Chemmakha Mohammed,Lazaar Mohamed^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-023-07608-z.pdf

Reference65 articles.

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2. Marastoni, N.; Giacobazzi, R.; Dalla Preda, M.: Data augmentation and transfer learning to classify malware images in a deep learning context. J. Comput. Virol. Hacking Tech. 17(4), 279–297 (2021)

3. Schrittwieser, S.; Katzenbeisser, S.; Kinder, J.; Merzdovnik, G.; Weippl, E.: Protecting software through obfuscation. ACM Comput. Surv. 49(1), 1–37 (2016)

4. Collberg, C.; Omborson, C.; Low, D.: A Taxonomy of Obfuscating Transformations. CiteSeer, Oxford (1997)

5. Marastoni, N.; Giacobazzi, R.; Dalla Preda, M.; A deep learning approach to program similarity. In: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES 2018 (New York, NY, USA: Association for Computing Machinery, 2018), 26 35.

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