SIMECK32/64的改进差分神经密码分析

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2023-12-02 DOI:10.1007/s11704-023-3261-z

Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li

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引用次数: 1

摘要

在这项研究中，我们开发了一个神经网络，旨在提高神经区分器的精度，并证明了它在某些回合中超过基于ddd的区分器的能力。为了将键恢复攻击的范围扩展到更多回合，我们一直在努力改进经典微分和神经区分。因此，我们已经成功地在SIMECK32/64上执行了实际的密钥恢复攻击，有效地将实际攻击阈值提高了两轮，使我们达到了17轮。

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Improved differential-neural cryptanalysis for round-reduced SIMECK32/64

In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.