Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers

被引:0
|
作者
Baksi, Anubhab [1 ]
Breier, Jakub [2 ,3 ]
Chen, Yi [4 ]
Dong, Xiaoyang [4 ]
机构
[1] Nanyang Technol Univ, Singapore, Singapore
[2] TU Graz SAL DES Lab, Silicon Austria Labs, Graz, Austria
[3] Graz Univ Technol, Graz, Austria
[4] Tsinghua Univ, Beijing, Peoples R China
来源
PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021) | 2021年
关键词
gimli; ascon; knot; chaskey; distinguisher; machine learning; differential;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 compared to the one using only 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 non-Markov ciphers through machine learning. Our idea here is to reduce a distinguishing problem to a classification problem, so that it can be efficiently managed by machine learning. As a proof of concept, we show several distinguishers for four high profile ciphers, each of which works with trivial complexity. In particular, we show differential distinguishers for 8-round Gimli-Hash, Gimli-Cipher and Gimli-Permutation; 3-round Ascon-Permutation; 10-round Knot-256 permutation and 12-round Knot-512 permutation; and 4-round Chaskey-Permutation. Finally, we explore more on choosing an efficient machine learning model and observe that only a three layer neural network can be used. Our analysis shows the attacker is able to reduce the complexity of finding distinguishers by using machine learning techniques.
引用
收藏
页码:176 / 181
页数:6
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