使用机器学习建模密码区分器

IF 1.5 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Journal of Cryptographic Engineering Pub Date : 2021-07-01 DOI:10.1007/s13389-021-00262-x

Carlo Brunetta, Pablo Picazo-Sanchez

引用次数: 2

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Modelling cryptographic distinguishers using machine learning

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Cryptographic Engineering COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

4.70

自引率

0.00%

发文量

期刊介绍： The Journal of Cryptographic Engineering (JCEN) presents high-quality scientific research on architectures, algorithms, techniques, tools, implementations and applications in cryptographic engineering, including cryptographic hardware, cryptographic embedded systems, side-channel attacks and countermeasures, and embedded security. JCEN serves the academic and corporate R&D community interested in cryptographic hardware and embedded security.JCEN publishes essential research on broad and varied topics including:Public-key cryptography, secret-key cryptography and post-quantum cryptographyCryptographic implementations include cryptographic processors, physical unclonable functions, true and deterministic random number generators, efficient software and hardware architecturesAttacks on implementations and their countermeasures, such as side-channel attacks, fault attacks, hardware tampering and reverse engineering techniquesSecurity evaluation of real-world cryptographic systems, formal methods and verification tools for secure embedded design that offer provable security, and metrics for measuring securityApplications of state-of-the-art cryptography, such as IoTs, RFIDs, IP protection, cyber-physical systems composed of analog and digital components, automotive security and trusted computing