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Article type: Research Article
Authors: Huang, Yinga; b | Li, Langa; b; * | Li, Dia; b | Li, Yongchaoc
Affiliations: [a] College of Computer Science and Technology, Hengyang Normal University, Hengyang, China | [b] Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China | [c] School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
Correspondence: [*] Corresponding author. Lang Li. Fax: +86 0734 3456; E-mail: [email protected].
Abstract: AND-Rotation-XOR (AND-RX) ciphers are known for its unique round function and excellent implementation performance. As a result, AND-RX ciphers are well suited for protecting sensitive information on resource-constrained devices. AND-RX ciphers need to be passed by rigorous cryptanalysis methods before practice. Integral cryptanalysis is one of the important cryptanalysis methods. MILP-based automated model is constructed to solve the integral cryptanalysis of AND-RX ciphers. The automated model usually consumes a long time when the block length and the number of round function components are large. In this paper, we design a neural distinguisher named IABC model for fast and efficient integral cryptanalysis. The IABC model learns to distinguish between ciphertext multisets to construct an integral distinguisher for AND-RX cipher, which ciphertext multisets from plaintext or random plaintexts. The IABC model is used for SIMON, SIMECK and SAND ciphers, which validates the neural distinguisher for AND-RX ciphers. The experimental results show that the IABC model is capable of expanding the number of rounds of integral distinguishers for AND-RX ciphers with certain accuracy. Therefore, IABC model can be effectively used for integral cryptanalysis of AND-RX ciphers. In addition, we discover that a larger number of active bits in the plaintext multiset results in a more accurate IABC model.
Keywords: AND-RX cipher, integral cryptanalysis, division property, neural distinguisher
DOI: 10.3233/JIFS-238122
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
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