Browse > Article
http://dx.doi.org/10.13089/JKIISC.2020.30.3.337

An Study on the Analysis of Design Criteria for S-Box Based on Deep Learning  

Kim, Dong-hoon (Korea University)
Kim, Seonggyeom (Korea University)
Hong, Deukjo (Chonbuk National University)
Sung, Jaechul (University of Seoul)
Hong, Seokhie (Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.30, no.3, 2020 , pp. 337-347 More about this Journal
Abstract
In CRYPTO 2019, Gohr presents that Deep-learning can be used for cryptanalysis. In this paper, we verify whether Deep-learning can identify the structures of S-box. To this end, we conducted two experiments. First, we use DDT and LAT of S-boxes as the learning data, whose structure is one of mainly used S-box structures including Feistel, MISTY, SPN, and multiplicative inverse. Surprisingly, our Deep-learning algorithms can identify not only the structures but also the number of used rounds. The second application verifies the pseudo-randomness of and structures by increasing the nuber of rounds in each structure. Our Deep-learning algorithms outperform the theoretical distinguisher in terms of the number of rounds. In general, the design rationale of ciphers used for high level of confidentiality, such as for military purposes, tends to be concealed in order to interfere cryptanalysis. The methods presented in this paper show that Deep-learning can be utilized as a tool for analyzing such undisclosed design rationale.
Keywords
Cryptanalysis; Deep-learning; Symmetric key; S-box structure;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 KWON, Donggeun, et al. Improving Non-Profiled Side-Channel Analysis Using Auto-Encoder Based Noise Reduction Preprocessing. Journal of the Korea Institute of Information Security & Cryptology, 2019, 29.3: 491-501.   DOI
2 GOHR, Aron. Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning. In: Annual International Cryptology Conference. Springer, Cham, 2019. p. 150-179.
3 MATSUI, Mitsuru. New block encryption algorithm MISTY. In: International Workshop on Fast Software Encryption. Springer, Berlin, Heidelberg, 1997. p. 54-68.
4 BIRYUKOV, Alex; PERRIN, Leo. On reverse-engineering S-Boxes with hidden design criteria or structure. In: Annual Cryptology Conference. Springer, Berlin, Heidelberg, 2015. p. 116-140.
5 LUBY, Michael; RACKOFF, Charles. How to construct pseudorandom permutations from pseudorandom functions. SIAM Journal on Computing, 1988, 17.2: 373-386.   DOI
6 PATARIN, Jacques. Generic attacks on Feistel schemes. In: International Conference on the Theory and Application of Cryptology and Information Security. Springer, Berlin, Heidelberg, 2001. p. 222-238.
7 GILBERT, Henri; MINIER, Marine. New results on the pseudorandomness of some blockcipher constructions. In: International Workshop on Fast Software Encryption. Springer, Berlin, Heidelberg, 2001. p. 248-266.
8 DAEMEN, Joan; RIJMEN, Vincent. The design of Rijndael. New York: Springer-verlag, 2002.
9 BIHAM, Eli; SHAMIR, Adi. Differential cryptanalysis of DES-like cryptosystems. Journal of CRYPTOLOGY, 1991, 4.1: 3-72.   DOI
10 MATSUI, Mitsuru. Linear cryptanalysis method for DES cipher. In: Workshop on the Theory and Application of of Cryptographic Techniques. Springer, Berlin, Heidelberg, 1993. p. 386-397.
11 DE MELLO, Flavio Luis; XEXEO, Jose AM. Identifying Encryption Algorithms in ECB and CBC Modes Using Computational Intelligence. J. UCS, 2018, 24.1: 25-42.
12 TAN, Cheng; JI, Qingbing. An approach to identifying cryptographic algorithm from ciphertext. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN). IEEE, 2016. p. 19-23.