{"title":"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","authors":"Gao Wang, Gaoli Wang, Siwei Sun","doi":"10.1049/2024/4097586","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"2024 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/4097586","citationCount":"0","resultStr":"{\"title\":\"A New (Related-Key) Neural Distinguisher Using Two Differences for Differential Cryptanalysis\",\"authors\":\"Gao Wang, Gaoli Wang, Siwei Sun\",\"doi\":\"10.1049/2024/4097586\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>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.</p>\\n </div>\",\"PeriodicalId\":50380,\"journal\":{\"name\":\"IET Information Security\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/4097586\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Information Security\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/2024/4097586\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Information Security","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/2024/4097586","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A New (Related-Key) Neural Distinguisher Using Two Differences for Differential Cryptanalysis
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.
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
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