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http://dx.doi.org/10.13089/JKIISC.2022.32.2.391

Analysis of Gohr's Neural Distinguisher on Speck32/64 and its Application to Simon32/64  

Seong, Hyoeun (Dept. of Financial Information Security, Kookmin University)
Yoo, Hyeondo (Dept. of Financial Information Security, Kookmin University)
Yeom, Yongjin (Dept. of Information Security, Cryptology and Mathematics, Kookmin University)
Kang, Ju-Sung (Dept. of Information Security, Cryptology and Mathematics, Kookmin University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.32, no.2, 2022 , pp. 391-404 More about this Journal
Abstract
Aron Gohr proposed a cryptanalysis method based on deep learning technology for the lightweight block cipher Speck. This is a method that enables a chosen plaintext attack with higher accuracy than the classical differential cryptanalysis. In this paper, by using the probability distribution, we analyze the mechanism of such deep learning based cryptanalysis and propose the results applied to the lightweight block cipher Simon. In addition, we examine that the probability distributions of the predicted values of the neural networks within the cryptanalysis working processes are different depending upon the characteristics of round functions of Speck and Simon, and suggest a direction to improve the efficiency of the neural distinguisher which is the core technology of Aron Gohr's cryptanalysis.
Keywords
Differential cryptanalysis; Deep learning; Block cipher; Neural distinguisher;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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