Cryptosystem Identification Scheme Based on ASCII Code Statistics

Abstract EN

In the field of information security, block cipher is widely used in the protection of messages, and its safety naturally attracts people’s attention.

The identification of the cryptosystem is the premise of encrypted data analysis.

It belongs to the category of attack analysis in cryptanalysis and has important theoretical significance and application value.

This paper focuses on the extraction of ciphertext features and the construction of cryptosystem identification classifiers.

The main contents and innovations of this paper are as follows.

Firstly, inspired by language processing, we propose the feature extraction scheme based on ASCII statistics of ciphertexts which decrease the dimension of data preprocessing.

Secondly, on the basis of previous work, we increase the types of block ciphers to eight, encrypt plaintext of the same sizes as experimental objects, and recognize the cryptosystem.

Thirdly, we use two machine learning classifiers to perform classification experiments including random forest and SVM.

The experimental results show that our scheme can not only improve the identification accuracy of 8 typical block cipher algorithms but also shorten the experimental time and reduce the computation load by greatly minimizing the dimension of the feature vector.

And the various evaluation indicators obtained by the scheme have been greatly improved compared with the existing published literature.