Cryptographic algorithm identification is aimed to analyze the potential feature information in ciphertext data when the ciphertext is known, which belongs to the category of cryptanalysis. This paper takes block cipher algorithm as the research object, and proposes a block cipher algorithm identification scheme based on hybrid random forest and logistic regression (HRFLR) model with the idea of ensemble learning. Based on the NIST randomness test feature extraction method, five block ciphers, AES, 3DES, Blowfish, CAST and RC2, are selected as the research object of cryptographic algorithm identification to carry out the ciphertext classification tasks. The experimental results show that, compared with the existing methods, the cryptographic algorithm identification scheme based on HRFLR proposed in this paper has higher accuracy and stability on binary classification and multi-class classification tasks. In the binary classification tasks of AES and 3DES, the identification accuracy of our proposed cryptographic algorithm identification scheme based on HRFLR can reach up to 74%, and the highest identification accuracy of the five classification tasks is 38%. Compared with the 54% and 28.8% accuracies of random forest-based identification scheme, the accuracy is increased by 37.04% and 18.06%, respectively. This result is significantly better than the 50% and 20% accuracies of random guessing scheme.

密码算法识别是在密文已知的情况下，分析密文数据中潜在的特征信息，属于密码分析的范畴。本文以分组密码算法为研究对象，结合集成学习的思想，提出了一种基于混合随机森林和逻辑回归（HRFLR）模型的分组密码算法识别方案。基于NIST随机性测试特征提取方法，选取AES、3DES、Blowfish、CAST和RC2五种分组密码作为密码算法识别的研究对象，开展密文分类任务。实验结果表明，与现有方法相比，本文提出的基于HRFLR的密码算法识别方案在二进制分类和多类分类任务上具有较高的准确性和稳定性。在AES和3DES的二进制分类任务中，我们提出的基于HRFLR的密码算法识别方案的识别准确率可以达到74%，五种分类任务的最高识别准确率为38%。与基于随机森林的识别方案的 54% 和 28.8% 的准确率相比，准确率分别提高了 37.04% 和 18.06%。该结果明显优于随机猜测方案的 50% 和 20% 准确度。我们提出的基于 HRFLR 的密码算法识别方案的识别准确率最高可达 74%，五种分类任务的最高识别准确率为 38%。与基于随机森林的识别方案的 54% 和 28.8% 的准确率相比，准确率分别提高了 37.04% 和 18.06%。该结果明显优于随机猜测方案的 50% 和 20% 准确度。我们提出的基于 HRFLR 的密码算法识别方案的识别准确率最高可达 74%，五种分类任务的最高识别准确率为 38%。与基于随机森林的识别方案的 54% 和 28.8% 的准确率相比，准确率分别提高了 37.04% 和 18.06%。该结果明显优于随机猜测方案的 50% 和 20% 准确度。