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Side Channel Analysis of SPECK Based on Transfer Learning

Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryptio...

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Autores principales: Zhang, Qingqing, Zhang, Hongxing, Cui, Xiaotong, Fang, Xing, Wang, Xingyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268767/
https://www.ncbi.nlm.nih.gov/pubmed/35808166
http://dx.doi.org/10.3390/s22134671
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author Zhang, Qingqing
Zhang, Hongxing
Cui, Xiaotong
Fang, Xing
Wang, Xingyang
author_facet Zhang, Qingqing
Zhang, Hongxing
Cui, Xiaotong
Fang, Xing
Wang, Xingyang
author_sort Zhang, Qingqing
collection PubMed
description Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK’s operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%.
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spelling pubmed-92687672022-07-09 Side Channel Analysis of SPECK Based on Transfer Learning Zhang, Qingqing Zhang, Hongxing Cui, Xiaotong Fang, Xing Wang, Xingyang Sensors (Basel) Article Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK’s operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%. MDPI 2022-06-21 /pmc/articles/PMC9268767/ /pubmed/35808166 http://dx.doi.org/10.3390/s22134671 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Qingqing
Zhang, Hongxing
Cui, Xiaotong
Fang, Xing
Wang, Xingyang
Side Channel Analysis of SPECK Based on Transfer Learning
title Side Channel Analysis of SPECK Based on Transfer Learning
title_full Side Channel Analysis of SPECK Based on Transfer Learning
title_fullStr Side Channel Analysis of SPECK Based on Transfer Learning
title_full_unstemmed Side Channel Analysis of SPECK Based on Transfer Learning
title_short Side Channel Analysis of SPECK Based on Transfer Learning
title_sort side channel analysis of speck based on transfer learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268767/
https://www.ncbi.nlm.nih.gov/pubmed/35808166
http://dx.doi.org/10.3390/s22134671
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