Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel Attacks
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...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10604855/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |