Classifying encryption algorithms using pattern recognition techniques

Cryptanalysis attempts identify the weaknesses in the algorithms used to encrypt code or the methods used to generate keys. In this study, we use pattern recognition techniques for identification of encryption algorithms for block ciphers. The following block cipher algorithms, DES, IDEA, AES, and RC operating in ECB mode were considered. Eight different classification techniques which are: Naïve Bayesian (NB), Support Vector Machine (SVM), neural network (MPL), Instance based learning (IBL), Bagging (Ba), AdaBoostM1, Rotaion Forest (RoFo), Decision Tree (C4.5) were used to identify the cipher text. This study aims to find the best classification algorithm to identify the cipher encryption method. The performance of each of the classifiers was presented, and the simulation results show that, in general, the RoFo classifier has the highest classification accuracy.