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Using deep learning to combine static and dynamic power analyses of cryptographic circuits
Author(s) -
Xu Jiming,
Heys Howard M.
Publication year - 2019
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2623
Subject(s) - side channel attack , leakage (economics) , computer science , cryptography , dynamic demand , power analysis , computer engineering , electronic circuit , leakage power , electronic engineering , embedded system , artificial neural network , power (physics) , algorithm , artificial intelligence , engineering , transistor , electrical engineering , physics , quantum mechanics , voltage , economics , macroeconomics
Summary Side‐channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side‐channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side‐channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short‐term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45‐nm library.