In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software ...In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software platforms. In this paper, we mainly present zero-correlation linear cryptanalysis on various versions of SIMON. Firstly, by using miss- in-the-middle approach, we construct zero-correlation linear distinguishers of SIMON, and zero-correlation linear attacks are presented based oi1 careful analysis of key recovery phase. Secondly, multidimensional zero-correlation linear attacks are used to reduce the data complexity. Our zero-correlation linear attacks perform better than impossible differential attacks proposed by Abed et al. in ePrint Report 2013/568. Finally, we also use the divide-and-conquer technique to improve the results of linear cryptanalysis proposed by Javad et al. in ePrint Report 2013/663.展开更多
At the Annual International Cryptology Conference in 2019,Gohr introduced a deep learning based cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64.One si...At the Annual International Cryptology Conference in 2019,Gohr introduced a deep learning based cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64.One significant challenge left unstudied by Gohr's work is the implementation of key recovery attacks on large-state block ciphers based on deep learning.The purpose of this paper is to present an improved deep learning based framework for recovering keys for large-state block ciphers.First,we propose a key bit sensitivity test(KBST)based on deep learning to divide the key space objectively.Second,we propose a new method for constructing neural distinguisher combinations to improve a deep learning based key recovery framework for large-state block ciphers and demonstrate its rationality and effectiveness from the perspective of cryptanalysis.Under the improved key recovery framework,we train an efficient neural distinguisher combination for each large-state member of SIMON and SPECK and finally carry out a practical key recovery attack on the large-state members of SIMON and SPECK.Furthermore,we propose that the 13-round SIMON64 attack is the most effective approach for practical key recovery to date.Noteworthly,this is the first attempt to propose deep learning based practical key recovery attacks on18-round SIMON128,19-round SIMON128,14-round SIMON96,and 14-round SIMON64.Additionally,we enhance the outcomes of the practical key recovery attack on SPECK large-state members,which amplifies the success rate of the key recovery attack in comparison to existing results.展开更多
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant Nos. 61272476, 61202420, and 61232009.
文摘In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software platforms. In this paper, we mainly present zero-correlation linear cryptanalysis on various versions of SIMON. Firstly, by using miss- in-the-middle approach, we construct zero-correlation linear distinguishers of SIMON, and zero-correlation linear attacks are presented based oi1 careful analysis of key recovery phase. Secondly, multidimensional zero-correlation linear attacks are used to reduce the data complexity. Our zero-correlation linear attacks perform better than impossible differential attacks proposed by Abed et al. in ePrint Report 2013/568. Finally, we also use the divide-and-conquer technique to improve the results of linear cryptanalysis proposed by Javad et al. in ePrint Report 2013/663.
基金Project supported by the National Natural Science Foundation of China(No.62206312)。
文摘At the Annual International Cryptology Conference in 2019,Gohr introduced a deep learning based cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64.One significant challenge left unstudied by Gohr's work is the implementation of key recovery attacks on large-state block ciphers based on deep learning.The purpose of this paper is to present an improved deep learning based framework for recovering keys for large-state block ciphers.First,we propose a key bit sensitivity test(KBST)based on deep learning to divide the key space objectively.Second,we propose a new method for constructing neural distinguisher combinations to improve a deep learning based key recovery framework for large-state block ciphers and demonstrate its rationality and effectiveness from the perspective of cryptanalysis.Under the improved key recovery framework,we train an efficient neural distinguisher combination for each large-state member of SIMON and SPECK and finally carry out a practical key recovery attack on the large-state members of SIMON and SPECK.Furthermore,we propose that the 13-round SIMON64 attack is the most effective approach for practical key recovery to date.Noteworthly,this is the first attempt to propose deep learning based practical key recovery attacks on18-round SIMON128,19-round SIMON128,14-round SIMON96,and 14-round SIMON64.Additionally,we enhance the outcomes of the practical key recovery attack on SPECK large-state members,which amplifies the success rate of the key recovery attack in comparison to existing results.
