Improved deep learning aided key recovery framework:applications to large-state block ciphers
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 on 18-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.
Deep learningLarge-state block cipherKey recoveryDifferential cryptanalysisSIMONSPECK
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