Classification of Encryption Algorithms using Fisher’s Discriminant Analysis

Abstract

Fisher’s Discriminant Analysis (FDA) is a method used in statistics and machine learning which can often lead to good classification between several populations by maximizing the separation between the populations. We will present some applications of FDA that discriminate between cipher texts in terms of a finite set of encryption algorithms. Specifically, we use ten algorithms, five each of stream and block cipher types. Our results display good classification with some of the features. In the present case we have little in terms of an existing standard; however, our limited study clearly shows that further exploration of this issue could be worthwhile.