Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks
Ahmed A.A. Hasan M.K. Aman A.H. Safie N. Islam S. Ahmed F.A. Ahmed T.E. Pandey B. Rzayeva L.
2024Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2024#12188435 - 188453 pp.
During the years 2018-2024, considerable advancements have been achieved in the use of deep learning for side channel attacks. The security of cryptographic algorithm implementations is put at risk by this. The aim is to conceptually keep an eye out for specific types of information loss, like power usage, on a chip that is doing encryption. Next, one trains a model to identify the encryption key by using expertise of the underpinning encryption algorithm. The encryption key is then recovered by applying the model to traces that were obtained from a victim chip. Deep learning is being used in many different fields in the past several years. Convolutional neural networks and recurrent neural networks, for instance, have demonstrated efficacy in text generation and object detection in images, respectively. In this paper, we have presented a review on deep learning models for encryption techniques against side channel attacks with a comparison table. Also, we have detailed the necessity of hybrid deep learning models for enhancing encryption techniques against these side channel attacks.
Convolutional neural networks , deep learning , encryption , review , side channel attacks
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Imam Alkadhim University College, Department of Computer Science, Baghdad, 10011, Iraq
Universiti Kebangsaan Malaysia, Research Center for Software Technology and Management (SOFTAM), Faculty of Information Science and Technology (FTSM), Bangi, 43600, Malaysia
Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, 56000, Malaysia
Prince Sattam Bin Abdulaziz University, College of Computer Engineering and Science, Computer Science Department, Al-Kharj, 16273, Saudi Arabia
Imam Abdulrahman Bin Faisal University, College of Science and Humanities-Jubail, Computer Science Department, Dammam, 35811, Saudi Arabia
Astana IT University, Department of Intelligent System and Cyber Security, Astana, 020000, Kazakhstan
Universiti Kebangsaan Malaysia
Imam Alkadhim University College
Universiti Kebangsaan Malaysia
Institute of Computer Science and Digital Innovation
Prince Sattam Bin Abdulaziz University
Imam Abdulrahman Bin Faisal University
Astana IT University
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