오채린, 서창호, 정수용, 김현일, 임우상,

Vol. 34, No. 6, pp. 1171-1188, 12월. 2024
10.13089/JKIISC.2024.34.6.1171, Full Text:
Keywords: Deep Learning, differential cryptanalysis, Neural distinguisher
Abstract

The need for secure encryption is growing as digital technology advances. Lightweight block cipher algorithms, essential for resource-limited environments like IoT, are increasingly analyzed using deep learning. Neural distinguishers applying neural networks to differential crypanalysis have been proposed to identify vulnerabilities effectively, though they suffer performance drops with more encryption rounds. To address this, enhancements like modifications in input data formats and the use of weight functions have been proposed. Recently, related-key neural distinguishers, which utilize differences in both ciphertext pairs and key pairs, have demonstrated improved performance. Despite these advancements, studies focusing on performance optimization for related-key neural distinguishers are still limited. In this paper, we analyze the impact of input data formats and weight functions proposed to improve the performance of neural distinguishers and explore how differences in output value distribution characteristics affect performance. Through this analysis, we identify a suitable weight function for the Simeck 32/64 algorithm and propose methods to enhance the performance of related-key neural distinguishers, presenting a new analytical perspective that the optimal weight function may vary depending on the type of distinguisher.

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