A New (Related-Key) Neural Distinguisher Using Two Differences for Differential Cryptanalysis

IF 1.3 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IET Information Security Pub Date : 2024-11-01 DOI:10.1049/2024/4097586

Gao Wang, Gaoli Wang, Siwei Sun

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引用次数: 0

Abstract

At CRYPTO 2019, Gohr showed the significant advantages of neural distinguishers over traditional distinguishers in differential cryptanalysis. At fast software encryption (FSE) 2024, Bellini et al. provided a generic tool to automatically train the (related-key) differential neural distinguishers for different block ciphers. In this paper, based on the intrinsic principle of differential cryptanalysis and neural distinguisher, we propose a superior (related-key) differential neural distinguisher that uses the ciphertext pairs generated by two different differences. In addition, we give a framework to automatically train our (related-key) differential neural distinguisher with four steps: difference selection, sample generation, training pipeline, and evaluation scheme. To demonstrate the effectiveness of our approach, we apply it to the block ciphers: Simon, Speck, Simeck, and Hight. Compared to the existing results, our method can provide improved accuracy and even increase the number of rounds that can be analyzed. The source codes are available in https://github.com/differentialdistinguisher/AutoND_New.

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利用两个差分进行差分密码分析的新型（相关密钥）神经区分器

在 CRYPTO 2019 上，Gohr 展示了神经区分器在差分密码分析中相对于传统区分器的显著优势。在 2024 年快速软件加密（FSE）大会上，Bellini 等人提供了一种通用工具，用于自动训练不同块密码的（相关密钥）差分神经区分器。在本文中，我们基于差分密码分析和神经区分器的内在原理，提出了一种更优越的（相关密钥）差分神经区分器，它使用由两种不同差异产生的密码文本对。此外，我们还给出了自动训练（相关密钥）差分神经区分器的框架，包括四个步骤：差分选择、样本生成、训练流水线和评估方案。为了证明我们的方法的有效性，我们将其应用于块密码：Simon、Speck、Simeck 和 Hight。与现有结果相比，我们的方法提高了准确性，甚至增加了可分析的回合数。源代码见 https://github.com/differentialdistinguisher/AutoND_New。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IET Information Security 工程技术-计算机：理论方法

CiteScore

3.80

自引率

7.10%

发文量

审稿时长

8.6 months

期刊介绍： IET Information Security publishes original research papers in the following areas of information security and cryptography. Submitting authors should specify clearly in their covering statement the area into which their paper falls. Scope: Access Control and Database Security Ad-Hoc Network Aspects Anonymity and E-Voting Authentication Block Ciphers and Hash Functions Blockchain, Bitcoin (Technical aspects only) Broadcast Encryption and Traitor Tracing Combinatorial Aspects Covert Channels and Information Flow Critical Infrastructures Cryptanalysis Dependability Digital Rights Management Digital Signature Schemes Digital Steganography Economic Aspects of Information Security Elliptic Curve Cryptography and Number Theory Embedded Systems Aspects Embedded Systems Security and Forensics Financial Cryptography Firewall Security Formal Methods and Security Verification Human Aspects Information Warfare and Survivability Intrusion Detection Java and XML Security Key Distribution Key Management Malware Multi-Party Computation and Threshold Cryptography Peer-to-peer Security PKIs Public-Key and Hybrid Encryption Quantum Cryptography Risks of using Computers Robust Networks Secret Sharing Secure Electronic Commerce Software Obfuscation Stream Ciphers Trust Models Watermarking and Fingerprinting Special Issues. Current Call for Papers: Security on Mobile and IoT devices - https://digital-library.theiet.org/files/IET_IFS_SMID_CFP.pdf