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A generic cryptographic algorithm identification scheme based on ciphertext features

JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2025年 89卷

作者： Li, Jiabao Sun, Hanlin Du, Zhanfei Wang, Yaxuan Yuan, Ke Jia, Chunfu Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Univ Macao Fac Sci & Technol Macau 519000 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Henan Univ Henan Prov Engn Res Ctr Spatial Informat Proc Kaifeng 475004 Peoples R China Nankai Univ Coll Cybersecur Tianjin 300350 Peoples R China

To assist relevant agencies in conducting security assessments of commercial cryptographic applications or establishing security monitoring and early warning mechanisms for cryptographic system, this paper proposes a generic cryptographic algorithm identification scheme based on ciphertext features and machine learning. The assessment agency generates a dataset with the information for testing and sends it to the testing server. Subsequently, the target agency server employs the cryptographic system to generate a ciphertext dataset, which is then transmitted to the testing server. By extracting features from the ciphertext and applying machine learning techniques, the cryptographic algorithms can be accurately identified on the testing server. Finally, the test results are generated and transmitted back to the assessment agency. This paper formally defines the scheme model and presents a detailed implementation. The scheme is primarily used in the security assessment of commercial cryptographic applications, allowing the assessment agency to analyze the obtained ciphertext files and determine whether the cryptographic algorithms meet specified requirements, as well as assess any potential risks. Notably, this approach avoids physical contact with cryptographic equipment and minimizes disruptions to the target agency's normal operations during the assessment.

关键词： cryptographic algorithm identification Machine learning Security assessment of commercial cryptographic applications cryptographic system evaluation

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cryptographic algorithm identification Using Machine Learning and Massive Processing

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IEEE LATIN AMERICA TRANSACTIONS 2016年第11期14卷 4585-4590页

作者： de Mello, F. L. Xexeo, J. A. M. Fed Univ Rio de Janeiro UFRJ Polytech Sch Rio De Janeiro Brazil Mil Inst Engn IME Rio De Janeiro Brazil

This paper presents a study on encryption algorithms identification by means of machine learning techniques. Plain text files, written in seven different languages, were encoded by seven cryptographic algorithm under ECB mode. The resulting cryptograms were submitted to a transformation so that it was possible to create metadata files. These files provide information for six data mining algorithms in order to identify the cryptographic algorithm used for encryption. The identification performance was evaluated and the language influence at the procedure was analyzed. The overall experiment involves many cryptograms, a great quantity of metadata, a huge time consuming computation, and therefore, it was employed a high performance computer. The successful identification for each mining algorithm is greater than a probabilistic bid, and there are several scenarios where algorithm identification reaches almost full recognition.

关键词： cryptographic algorithm identification Data mining Machine intelligence Parallel computing

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A multi-layer composite identification scheme of cryptographic algorithm based on hybrid random forest and logistic regression model

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 1131-1147页

作者： Yuan, Ke Huang, Yabing Du, Zhanfei Li, Jiabao Jia, Chunfu Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Henan Prov Engn Res Ctr Spatial Informat Proc Kaifeng 475004 Peoples R China Henan Rural Credit Union Zhengzhou 450018 Peoples R China Univ Macao Fac Sci & Technol Macau 519000 Peoples R China Henan Yinzhu Secur Technol Zhengzhou 450003 Peoples R China Nankai Univ Coll Cybersecur Tianjin 300350 Peoples R China

cryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification

关键词： Cryptanalysis cryptographic algorithm identification Ensemble learning Hybrid random forest and logistic regression Cluster division identification Single-layer identification

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Machine Learning Applied to the Recognition of cryptographic algorithms Used for Multimedia Encryption

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IEEE LATIN AMERICA TRANSACTIONS 2017年第7期15卷 1301-1305页

作者： Barbosa, Flavio Mendonca Vidal, A. R. S. F. Almeida, H. L. S. de Mello, F. L. IBM Brazil Sao Paulo Brazil Univ Fed Rio de Janeiro Dept Engn Eletron & Comp Escola Politecn Rio de Janeiro Brazil Univ Fed Rio de Janeiro Polytech Sch Elect & Comp Dept Rio De Janeiro Brazil

This paper presents a study of encrypted multimedia files in order to identify the encryption algorithm. Audio and video files were encoded with distinct cryptographic algorithms and then metadata were extracted from these cryptograms. The algorithm identification is obtained by using data mining techniques. Therefore, the procedure first stage performs the encryption of audio and video files using DES, Blowfish, RSA, and RC4 algorithms. Then, the encrypted files were submitted to the data mining algorithms: J48, FT, PART, Complement Naive Bayes, and Multilayer Perceptron classifiers. The resulting confusion matrices compiled into charts and it was possible to notice that the percentage of identification for each of the algorithms is greater than a probabilistic bid. There are several scenarios where algorithm identification reaches almost full recognition.

关键词： cryptographic algorithm identification data mining machine intelligence

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An Approach to Identifying cryptographic algorithm from Ciphertext 8

An Approach to Identifying Cryptographic Algorithm from Ciph...

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8th IEEE International Conference on Communication Software and Networks (ICCSN)

作者： Tan, Cheng Ji, Qingbing Sci & Technol Commun Secur Lab Chengdu Sichuan Provinc Peoples R China

ISBN: (纸本)9781509017812

cryptographic algorithm plays a significant role in a cryptosystem, which protects those sensitive and private data from been obtained by some malicious attackers. Actually, the details about the cryptographic algorithm applied in a cryptosystem are often unknown to one cryptanalyst. When a cryptanalyst works on cryptanalysis, he will have much trouble if he doesn't know anything about the used cryptographic algorithm. In this paper, we introduce an approach to identifying the cryptographic algorithm with no other information but ciphertext. Firstly, we present the whole implementation architecture of our identification system. Then we apply our identification system in identifying 5 common block ciphers, namely AES, Blowfish, 3DES, RC5 and DES. Through analyzing the experiment results, we conclude that the identification rate can obtain around 90% if keys are the same for training and testing ciphertexts. When we use different keys for training and testing ciphertexts, we can still identify AES from anyone of the other 4 cryptographic algorithms with a high identification rate in one to one identification.

关键词： identification system cryptographic algorithm identification machine learning SVM classifier

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A Block Cipher algorithm identification Scheme Based on Hybrid Random Forest and Logistic Regression Model

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NEURAL PROCESSING LETTERS 2023年第3期55卷 3185-3203页

作者： Yuan, Ke Huang, Yabing Li, Jiabao Jia, Chunfu Yu, Daoming Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Nankai Univ Coll Cybersecur Tianjin 300350 Peoples R China

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.

关键词： cryptographic algorithm identification Ensemble learning Random forest Logistic regression

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An Approach to Identifying cryptographic algorithm from Ciphertext

An Approach to Identifying Cryptographic Algorithm from Ciph...

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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Cheng Tan Qingbing Ji Science and Technology on Communication Security Laboratory

ISBN: (纸本)9781509017829

cryptographic algorithm plays a significant role in a cryptosystem,which protects those sensitive and private data from been obtained by some malicious ***,the details about the cryptographic algorithm applied in a cryptosystem are often unknown to one *** a cryptanalyst works on cryptanalysis,he will have much trouble if he doesn’t know anything about the used cryptographic *** this paper,we introduce an approach to identifying the cryptographic algorithm with no other information but ***,we present the whole implementation architecture of our identification *** we apply our identification system in identifying 5 common block ciphers,namely AES,Blowfish,3DES,RC5 and *** analyzing the experiment results,we conclude that the identification rate can obtain around 90%if keys are the same for training and testing *** we use different keys for training and testing ciphertexts,we can still identify AES from anyone of the other 4 cryptographic algorithms with a high identification rate in one to one identification.

关键词： identification system cryptographic algorithm identification machine learning SVM classifier

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

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1110页

作者： Yuan, Ke Yu, Daoming Feng, Jingkai Yang, Longwei Jia, Chunfu Huang, Yiwang Henan Univ Sch Comp & Informat Engn Kaifeng Henan Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Henan Peoples R China Henan Univ Int Educ Coll Zhengzhou Henan Peoples R China Nankai Univ Coll Cybersecur Tianjin Peoples R China Tongren Univ Sch Data Sci Tongren Guizhou Peoples R China

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 classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

关键词： cryptographic algorithm identification Machine learning Randomness test Random forest algorithm K-nearest neighbor algorithm

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FoC: Figure out the cryptographic Functions in Stripped Binaries with LLMs

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Xiuwei Shang Guoqiang Chen Shaoyin Cheng Shikai Guo Yanming Zhang Weiming Zhang Nenghai Yu University of Science and Technology of China Hefei China QI-ANXIN Technology Research Institute Beijing China University of Science and Technology of China Anhui Province Key Laboratory of Digital Security Hefei China Dalian Maritime University The Dalian Key Laboratory of Artificial Intelligence Dalian China

Analyzing the behavior of cryptographic functions in stripped binaries is a challenging but essential task, which is crucial in software security fields such as malware analysis and legacy code inspection. However, the inherent high logical complexity of cryptographic algorithms makes their analysis more difficult than that of ordinary code, and the general absence of symbolic information in binaries exacerbates this challenge. Existing methods for cryptographic algorithm identification frequently rely on data or structural pattern matching, which limits their generality and effectiveness while requiring substantial manual effort. In response to these challenges, we present FoC (Figure out the cryptographic functions), a novel framework that leverages large language models (LLMs) to identify and analyze cryptographic functions in stripped *** FoC, we first build an LLM-based generative model (FoC-BinLLM) to summarize the semantics of cryptographic functions in natural language form, which is intuitively readable to analysts. Subsequently, based on the semantic insights provided by FoC-BinLLM, we further develop a binary code similarity detection model (FoC-Sim), which allows analysts to effectively retrieve similar implementations of unknown cryptographic functions from a library of known cryptographic functions. The predictions of generative model like FoC-BinLLM are inherently difficult to reflect minor alterations in binary code, such as those introduced by vulnerability patches. In contrast, the change-sensitive representations generated by FoC-Sim compensate for the shortcomings to some extent. To support the development and evaluation of these models, and to facilitate further research in this domain, we also construct a comprehensive cryptographic binary dataset and introduce an automatic method to create semantic labels for extensive binary functions. Our evaluation results are promising. FoC-BinLLM outperforms ChatGPT by 14.61% on the ROUGE-L score,

关键词： Binary Code Summarization cryptographic algorithm identification Binary Code Similarity Detection Large Language Models

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