Cryptosystem Recognition Scheme Based on Convolution Features

Cryptosystem recognition is an important link in cryptanalysis, and ciphertext feature extraction is very important in cryptosystem recognition. In this paper, a ciphertext feature extraction method based on linear transformation and convolution sampling is proposed. A large number of plaintext data are encrypted as the data set, and a random forest is used to construct a classifier to recognize the two-class classification and multi-class classification of four cryptographic systems, such as AES, 3DES, Blowfish and RSA. The experimental results show that the recognition performance of convolution features is better and the accuracy of recognition in multi-class classification scene is improved. The accuracy of two class classification recognition is more than 80% and that of multi-class classification recognition is more than 70%.