Block Ciphers Classification Based on Random Forest

Discriminant analysis is an important analytical method in cryptanalysis. When only known ciphertext, the encryption algorithm for identifying and classifying is an important part of distinguishing analysis. In this paper, a random forest classifier is used to classify eight block ciphers of ECB mode and eight block ciphers of CBC mode. By designing a feature based on ciphertext recombination and location-specific, the results show that the designed features can effectively extract the information of ciphertext data. In ECB mode, 8 algorithms can be successfully classified with an accuracy of more than 87%. In CBC mode, it can also be classified with higher accuracy than random.