Using deep learning to combine static and dynamic power analyses of cryptographic circuits. (20th March 2019)

• Record Type: Journal Article
• Authors: Xu, Jiming; Heys, Howard M.
• Is Part Of: International journal of circuit theory and applications. Volume 47:Number 6(2019)
• Page Start: 971
• Page End: 990
• Publication Date: 2019-03-20
• Subjects: block ciphers -- deep learning -- lightweight ciphers -- LSTM -- neural network -- side‐channel attacks -- static power -- template attacks; Electric circuit analysis -- Periodicals; 621.319205
• DOI: 10.1002/cta.2623
• Languages: English
• ISSNs: 0098-9886

Abstract

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. Abstract : In this paper, we use the deep neural network technique to combine the benefits of both static and dynamic power when applying a power analysis attack to a cryptographic circuit. The approach replaces the classifier in template attacks, and instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources with a structural algorithm.