The Research of Cryptosystem Recognition Based on Randomness Test’s Return Value

Zhao, Zhicheng; Zhao, Yongqiang; Liu, Fengmei

doi:10.1007/978-3-030-00015-8_1

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…The first approach achieves an accuracy of 100% with a text length of 200, the second 71% with a text length of 155 and the third 100% with a text length of 155. Zhao et al (2018) extracted 54 features from 15 different NIST 800-22 (Rukhin et al, 2010) randomness tests. The efficiency was tested in several 10-fold-cross-validation SVM one-to-one classifiers for six modern block ciphers (AES, Blowfish, Camellia, DES, 3DES and IDEA).…”

Section: Related Workmentioning

confidence: 99%

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Leierzopf¹,

Kopal²,

Esslinger³

et al. 2021

Linköping Electronic Conference Proceedings

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Cryptanalysis of enciphered documents typically starts with identifying the cipher type. A large number of encrypted historical documents exists, whose decryption can potentially increase the knowledge of historical events. This paper investigates whether machine learning can support the cipher type classification task when only ciphertexts are given. A selection of engineered features for historical ciphertexts and various machine-learning classifiers have been applied for 56 different cipher types specified by the American Cryptogram Association. Different neuronal network models were empirically evaluated. Our best-performing model achieved an accuracy of 80.24% which improves the current state of the art by 37%. Accuracy is calculated by dividing the total number of samples by the number of true positive predictions. The software-suite is published under the name "Neural Cipher Identifier (NCID)".

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Section: Related Workmentioning

confidence: 99%

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Leierzopf¹,

Kopal²,

Esslinger³

et al. 2021

Linköping Electronic Conference Proceedings

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“…In 2018, Mello and Jam found that the proposed algorithm can almost completely identify all selected ciphers by analysing ciphertext files encrypted by seven cipher algorithms, ARC4, Blowfish, DES, Rijdael, RSA, Serpent and Twofish under the two modes of ECB and CBC, indicating that the ECB mode has higher identification accuracy than CBC [9]. In 2019, Zhao et al proposed a cryptographic algorithm identification scheme based on randomness detection [10], which proved that randomness detection can effectively extract ciphertext features. In the same year, Arvind and Ram proposed a method for identifying and isolating image-encrypted communication traffic using bit-plane image features and fuzzy decision criteria that use the same key encryption [?…”

Section: Introductionmentioning

confidence: 99%

Identification of Block Cipher Algorithms Using Multi-Layer Perception Algorithm

Yuan,

Yu,

Yang

et al. 2023

Preprint

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Cryptographic algorithm identification is the process of distinguishing or identifying the cryptographic algorithm by analysing the potential information of various features in the ciphertext under the condition of known ciphertext, which is the basis of cryptanalysis work. To solve the worse identification accuracy and stability problem of the single-layer scheme in cryptographic algorithm identification work as the complexity of ciphertext enhances, the interference between ciphertext data and the number of encryption algorithms increases, we propose a cryptographic algorithm identification scheme of block cipher algorithm using deep learning algorithm Multi-Layer Perception (MLP) in this paper. In this scheme, 15 NIST randomness test methods are used to extract ciphertext feature, and 10 meaningful features are selected as input to MLP classifier to make predictions. In the ciphertext-only scenario, five block cipher algorithms, AES, 3DES, Blowfish, CAST, and RC2, are selected for the study of cryptographic algorithm identification, then binary classification and multiclassification identification are carried out. The experimental results demonstrate that, when the size of the ciphertext files and other experimental conditions are the same as for the five conventional machine learning models, the proposed scheme has superior accuracy and stability. Among them, the average identification accuracy of binary classification is 76.5% for ciphertext files of 1kB to 512kB size, which is 35.3%, 37.5%, 34.2%, 38.5%, and 41.6% higher than that of the traditional classical machine learning models SVM, GNB, KNN, RF, and LR respectively. The average identification accuracy of the multiclassification is 36.2%, which is significantly higher than the other five classical machine learning algorithms. When the ciphertext file sizes are different, among the identification rates of the six models fluctuate, the MLP model has the greatest stability and the least amount of influence.

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“…Related work Other works propose to distinguish between random numbers generated with block ciphers [2,9,11,14,15,25,29] of which a vast majority extract features coming from the statistical tests proposed by NIST (NIST STS) [5] and use them as inputs of ML algorithms. While the documen-tation provided by the NIST does not provide any formal security analysis [13], Woodgate et al [28] carry out an indepth security review.…”

Section: Introductionmentioning

confidence: 99%

“…To extract features from NIST STS to distinguish between random data generated from block ciphers, Zhao et al [29] use (SVM). They use OpenSSL to generate ciphertexts from AES, Camellia, Blowfish, DES, IDEA and TDEA algorithms.…”

Section: Introductionmentioning

confidence: 99%

Modelling cryptographic distinguishers using machine learning

Brunetta

Picazo-Sanchez

2021

J Cryptogr Eng

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Cryptanalysis is the development and study of attacks against cryptographic primitives and protocols. Many cryptographic properties rely on the difficulty of generating an adversary who, given an object sampled from one of two classes, correctly distinguishes the class used to generate that object. In the case of cipher suite distinguishing problem, the classes are two different cryptographic primitives. In this paper, we propose a methodology based on machine learning to automatically generate classifiers that can be used by an adversary to solve any distinguishing problem. We discuss the assumptions, a basic approach for improving the advantage of the adversary as well as a phenomenon that we call the “blind spot paradox”. We apply our methodology to generate distinguishers for the NIST (DRBG) cipher suite problem. Finally, we provide empirical evidence that the distinguishers might statistically have some advantage to distinguish between the DRBG used.

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The Research of Cryptosystem Recognition Based on Randomness Test’s Return Value

Cited by 4 publications

References 12 publications

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Identification of Block Cipher Algorithms Using Multi-Layer Perception Algorithm

Modelling cryptographic distinguishers using machine learning

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