Block Cipher Algorithms Identification Scheme Based on KFDA

Cryptanalysis requires the identification of cryptographic algorithms, yet in practice, analysts often lack a clear understanding of the algorithms used, leading to ineffective analysis. This paper takes block cipher algorithm as the research object. According to different block cipher algorithms employ dis-tinct structures, cryptographic components, parameters, and key expansion methods, resulting in variations in the distribution of Hamming weights of ciphertexts generated by different algorithms. This paper proposes a Hamming weight-based ciphertext feature extraction method. Employing the dimensionality reduction mapping method with Gaussian kernel to process ciphertext feature data, we propose a block cipher algorithm identification scheme based on Kernel Fisher Discriminant Analysis (KFDA) and Random Forest. Eight block ciphers including AES, DES, SM4, etc. were selected as the experimental objects. In fixed and random key, a total of 144,000 encrypted ciphertext files of eight block cipher algorithms were constructed for algorithm identification. Experimental results demonstrate that compared to existing research, the cryptographic algorithm identification scheme proposed in this paper exhibits higher performance in binary classification and eight-class classification. In fixed key, the binary classification accuracy of block cipher algorithm is about 88%, and the eight-classification accuracy is about 48%, which is 8% and 10% higher than the average accuracy of existing research. In the random key, the accuracy of binary classification is about 72%, and the accuracy of eight classification is about 41%, Compared with the average accuracy of the existing research, the accuracy is increased by 5% and 18% respectively.