Guarding Against the Unknown: Deep Transfer Learning for Hardware Image-Based Malware Detection
Author:
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41635-024-00146-6.pdf
Reference48 articles.
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3. Sayadi H, Patel N, Sai Manoj PD, Sasan A, Rafatirad S, Homayoun H (2018) Ensemble learning for effective run-time hardware-based malware detection: A comprehensive analysis and classification. In: 2018 55th ACM/ESDA/IEEE design automation conference (DAC), pp 1–6. https://doi.org/10.1109/DAC.2018.8465828
4. Tang A, Sethumadhavan S, Stolfo SJ (2014) Unsupervised anomaly-based malware detection using hardware features. In: Stavrou A, Bos H, Portokalidis G (eds) Research in attacks, intrusions and defenses. Springer, Cham, pp 109–129
5. He Z, Rezaei A, Homayoun H, Sayadi H (2022) Deep neural network and transfer learning for accurate hardware-based zero-day malware detection. In: Proceedings of the great lakes symposium on VLSI 2022. GLSVLSI ’22, pp 27–32. Association for Computing Machinery, New York. https://doi.org/10.1145/3526241.3530326
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