Abstract: Based on the known ciphertext, cryptosystem identification is a process of identifying cryptographic algorithms by analyzing the potential feature information in ciphertext data. This paper presents a recognition scheme of sequential cryptosystem based on Hamming weight. This scheme generates labeled ciphertext feature vectors by calculating the Hamming weight of ciphertext blocks of different lengths. LDA dimensionality reduction technique is used to reduce the dimensionality of feature vectors, so as to optimize the extraction and utilization efficiency of data information. Finally, fully connected neural network is used to identify the feature vector after dimensionality reduction. The experimental results show that the proposed scheme can effectively perform two classification recognition experiments and eight classification recognition experiments on 8 stream cipher algorithms such as ZUC, Salsa20 and Decimv2, and achieve good recognition results. The average recognition rate of twoclass and eightclass recognition experiments is 99.29% and 79.12% respectively. Compared with the existing research, the accuracy of this scheme is improved by 16.29% compared with the existing literature with a small amount of ciphertext data.

Key words: cryptosystem recognition, stream cipher algorithm, linear discriminant analysis, fully connected neural networks, algorithm recognition

摘要： 密码体制识别是基于密文已知的情况下，通过分析密文数据中潜在的特征信息完成密码算法识别的过程.提出了一种基于汉明重量的序列密码体制识别方案.该方案通过计算不同长度密文块的汉明重量，生成带有标签的密文特征向量；运用LDA(linear discriminant analysis)降维技术对特征向量进行降维，从而优化数据信息的提取与利用效率；最后利用全连接神经网络对降维后的特征向量进行识别.实验结果表明，该方案能够有效地对ZUC,Salsa20,Decimv2等8种序列密码算法进行二分类识别实验和八分类识别实验，取得较好的识别效果.二分类识别实验的平均识别率为99.29%，八分类识别实验的平均识别率为79.12%.与现有研究相比，该方案在较少的密文数据量下，相较于现有文献准确率提升了16.29%.

关键词: 密码体制识别, 序列密码算法, LDA降维算法, 全连接神经网络, 算法识别