基于熵源验证的分组密码识别方案OA

The existing cryptographic algorithm identification schemes are mainly based on informa-tion entropy and randomness testing methods to design ciphertext features,which has the problem of low classification accuracy.In this paper,ciphertext features are extracted according to the entropy es-timation method,and five common machine learning algorithms including logistic regression,support vector machine,and decision tree are used to conduct classification experiments on five block ciphers DES,AES,3DES,Blowfish,and CAST.The experimental results show that the recognition scheme based on entropy source validation can effectively distinguish the working modes of block ciphers,with a classification accuracy of up to 99%.Meanwhile,the binary classification recognition accuracy of DES and AES with ECB mode is as high as 95%,and the recognition accuracy of the five classification experiments reaches 62.7%,outperfoming the 75%and 52%achieved by schemes relying solely on randomness detection.This research shows that the use of entropy source verification method can en-rich the ciphertext feature library and improve the recognition accuracy of cryptographic algorithms.