A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm.

ABSTRACT: Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher cla

SUBMITTER: Yuan K 

PROVIDER: S-EPMC9575859 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature