Identification on the structures of block ciphers using machine learning - Ruiqi XIA; Manman LI; Shaozhen CHEN (网络与信息安全学报)
Cryptographic identification is a critical aspect of cryptanalysis and a fundamental premise for key recovery.With the advancement of artificial intelligence, cryptanalysis based on machine learning has become increasingly mature, providing more effective methods and valuable insights for cryptographic identification.The distinguishability experiments were performed based on the Machine Learning to identify the structures of block ciphers in conditions of random keys.The identification of two structures of block ciphers from theoretical and experimental angles was studied.The differences of features in two structures’ cipher texts have been deduced by introducing the runs distribution index, feature distribution functions, KL-divergence, etc.After completing the feasibility research, experiments to identify the structures of two block ciphers using two Machine Learning models and the runs distribution index were conducted.The experiments were divided into two groups: single algorithm group and mixture algorithms group.It is found that the accuracy of both groups are more than 80%, which is around 40% higher than former work.The problem of identifying the structures of Block Ciphers in the conditions of random keys is solved in detail.Meanwhile, differences between the two structures of block ciphers are verified, which can serve as a reference for the design of cryptography algorithms.