{"title":"Conditional differential analysis on the KATAN ciphers based on deep learning","authors":"Dongdong Lin, Manman Li, Zezhou Hou, Shaozhen Chen","doi":"10.1049/ise2.12099","DOIUrl":null,"url":null,"abstract":"<p>KATAN ciphers are block ciphers using non-linear feedback shift registers. In this study, the authors improve the results of conditional differential analysis on KATAN by using deep learning. Multi-differential neural distinguishers are built to improve the accuracy of the neural distinguishers and increase the number of its rounds. Moreover, a conditional differential analysis framework is proposed based on deep learning with the multi-differential neural distinguishers, resulting in a significant improvement than the previous. We present a practical key recovery attack on the 97-round KATAN32 with 2<sup>15.5</sup> data complexity and 2<sup>20.5</sup> time complexity. The attack of the 82-round KATAN48 and 70-round KATAN64 are also presented as the best known practical results.</p>","PeriodicalId":50380,"journal":{"name":"IET Information Security","volume":"17 3","pages":"347-359"},"PeriodicalIF":1.3000,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ise2.12099","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Information Security","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ise2.12099","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}
引用次数: 0
Abstract
KATAN ciphers are block ciphers using non-linear feedback shift registers. In this study, the authors improve the results of conditional differential analysis on KATAN by using deep learning. Multi-differential neural distinguishers are built to improve the accuracy of the neural distinguishers and increase the number of its rounds. Moreover, a conditional differential analysis framework is proposed based on deep learning with the multi-differential neural distinguishers, resulting in a significant improvement than the previous. We present a practical key recovery attack on the 97-round KATAN32 with 215.5 data complexity and 220.5 time complexity. The attack of the 82-round KATAN48 and 70-round KATAN64 are also presented as the best known practical results.
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