Cryptology ePrint Archive: Report 2020/571

Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers

Anubhab Baksi and Jakub Breier and Xiaoyang Dong and Chen Yi

Abstract: At CRYPTO 2019, Gohr first introduces the deep learning based cryptanalysis on round-reduced SPECK. Using a deep residual network, Gohr trains several neural network based distinguishers on 8-round SPECK-32/64. The analysis follows an `all-in-one' differential cryptanalysis approach, which considers all the output differences effect under the same input difference.

Usually, the all-in-one differential cryptanalysis is more effective than that only uses one single differential trail. However, when the cipher is non-Markov or its block size is large, it is usually very hard to fully compute. Inspired by Gohr's work, we try to simulate the all-in-one differentials for such non-Markov ciphers through deep learning. As proof of works, we trained several distinguishing attacks following machine learning simulated all-in-one differential approach. We present 8-round differntial distinguishers for Gimli-Hash and Gimli-Cipher, each with trivial complexity. Finally, we explore more on choosing an efficient machine learning model and show a three layer neural network can be used.

Category / Keywords: secret-key cryptography / gimli, distinguisher, machine learning, differential

Date: received 15 May 2020, last revised 16 May 2020

Contact author: anubhab001 at e ntu edu sg,jbreier@ntu edu sg,xiaoyangdong@tsinghua edu cn,chenyi19@mails tsinghua edu cn

Available format(s): PDF | BibTeX Citation

Version: 20200516:103403 (All versions of this report)

Short URL: ia.cr/2020/571


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