Windows and IoT malware visualization and classification with deep CNN and Xception CNN using Markov images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10844-022-00734-4.pdf
Reference48 articles.
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2. Amin, M., Tanveer, T. A., Tehseen, M., Khan, M., Khan, F. A., & Anwar, S. (2020). Static malware detection and attribution in android byte-code through an end-to-end deep system. Future Generation Computer Systems, 102, 112–126. https://doi.org/10.1016/j.future.2019.07.070
3. Amin, M., Shehwar, D., Ullah, A., Guarda, T., Tanveer, T. A., & Anwar, S. (2020). “A deep learning system for health care IoT and smartphone malware detection,” Neural Comput & Applic. https://doi.org/10.1007/s00521-020-05429-x
4. Anandhi, V., Vinod, P., & Menon, V. G. (2021). “Malware visualization and detection using DenseNets,” Pers Ubiquit Comput. https://doi.org/10.1007/s00779-021-01581-w.
5. Andresini, G., Appice, A., De Rose, L., & Malerba, D. (2021). GAN augmentation to deal with imbalance in imaging-based intrusion detection. Future Generation Computer Systems, 123, 108–127. https://doi.org/10.1016/j.future.2021.04.017
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