The Research of Cryptosystem Recognition Based on Randomness Test’s Return Value

Feature extraction of ciphertext is a key procedure in cryptosystem recognition task. Varieties of ciphertext’s features are proposed in exited literatures, while feature based on randomness test has derived little attention. In this paper, by segmenting ciphertexts and changing parameter of randomness test, we propose 54 features of ciphertext based on NIST’s 15 randomness tests. As a measure of these features, we choose support vector machine as classifier algorithm to verify its performance in cryptosystem recognition. In experimental settings, we consider 15 situations of 6 cryptosystems’ one to one recognition. The experimental results demonstrate that the application of randomness test in cryptosystem recognition is feasible and necessary. Most of proposed features reach better recognition accuracies than random recognition, which indicates that randomness tests are applicable for cryptosystem recognition applications. And we also conduct further analysis: (a) analyze some features’ recognition performance, and find the relation of some feature’s recognition accuracies and cryptosystem. (b) compared with existed features, part of new features maintain high recognition accuracy with lower dimension and smaller data storage space.