Cryptosystem Identification Scheme Based on ASCII Code Statistics

Zhang, Wenyu; Zhao, Yongqiang; Fan, SiJie

doi:10.1155/2020/8875864

Cited by 9 publications

(11 citation statements)

References 14 publications

(8 reference statements)

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“…In [11] also generative adversarial networks have been employed to break these traditional cryptosystems. Machine learning algorithms and classification skills have been used to detect cryptographic algorithms from cipher texts in the works of [12] and [13]. Classifiers were trained using known cipher texts produced by a collection of six widely used cryptographic methods.…”

Section: Related Work Of Use Machine Learning In Cryptanalysismentioning

confidence: 99%

“…Coupled map lattice [63], a dynamical system with discrete time and discrete space, is employed in the manner stated by (12). This system has a long enough period to be employed in crypto-systems, and its output is transformed to integer numbers using (13). The generated numbers are then used to conduct right cyclic shifts to the image's rows and up cyclic shifts to the image's columns.…”

Section: ) Coupled Map Lattice(cml)mentioning

confidence: 99%

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Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment

et al. 2022

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Resistance to differential cryptanalysis is a fundamental security requirement for symmetric block ciphers, and recently, deep learning has attracted the interest of cryptography experts, particularly in the field of block cipher cryptanalysis, where the bulk of these studies are differential distinguisher based black-box attacks. This paper provides a deep learning-based decryptor technique for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing cipher text pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plain text from the ciphered text without any P-box expertise. The evaluation of testing methods has been conceptualized as a regression task in which neural networks are supervised using a variety of parameters such as variations between input and output, number of iterations, and P-box generation patterns. On the other hand, the transfer learning skills demonstrated in this study indicate that discovering suitable testing models from the ground is also achievable using our model with optimum prior cryptographic expertise, where we contribute the results of deep learning in the field of deep learning based differential cryptanalysis development.Various experiments were performed on discrete and continuous chaotic and non-chaotic permutation patterns, and the best-performing model had an MSE of 1.8217e −04 and an R 2 of 1, demonstrating the practicality of the suggested technique.

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Section: Related Work Of Use Machine Learning In Cryptanalysismentioning

confidence: 99%

Section: ) Coupled Map Lattice(cml)mentioning

confidence: 99%

Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment

et al. 2022

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“…One popular use case of machine learning is to distinguish or identify encryption algorithms based on ciphertext data. These approaches have been used to analyze data encrypted by popular block ciphers such as AES, 3DES, Blowfish and Camellia [14]- [17]. Cryptanalysts have also attempted to use machine learning in a straightforward manner -performing decryption of ciphertexts without knowledge of the secret key.…”

Section: Background a Related Workmentioning

confidence: 99%

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

et al. 2021

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One of the main security requirements for symmetric-key block ciphers is resistance against differential cryptanalysis. This is commonly assessed by counting the number of active substitution boxes (S-boxes) using search algorithms or mathematical solvers that incur high computational costs. These costs increase exponentially with respect to block cipher size and rounds, quickly becoming inhibitive. Conventional S-box enumeration methods also require niche cryptographic knowledge to perform. In this paper, we overcome these problems by proposing a data-driven approach using deep neural networks to predict the number of active S-boxes. Our approach trades off exactness for real-time efficiency as the bulk of computational work is brought over to pre-processing (training). Active S-box prediction is framed as a regression task whereby neural networks are trained using features such as input and output differences, number of rounds, and permutation pattern. We first investigate the feasibility of the proposed approach by applying it on a reduced (4-branch) generalized Feistel structure (GFS) cipher. Apart from optimizing a neural network architecture for the task, we also explore the impact of each feature and its representation on prediction error. We then extend the idea to 64-bit GFS ciphers by first training neural networks using data from five different ciphers before using them to predict the number of active S-boxes for TWINE, a lightweight block cipher. The best performing model achieved the lowest root mean square error of 1.62 and R 2 of 0.87, depicting the feasibility of the proposed approach.INDEX TERMS Active S-boxes, block cipher, cryptanalysis, deep learning, differential cryptanalysis, lightweight cryptography, neural networks, TWINE

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“…Most of the studies on manual extraction of ciphertext features have extracted statistical features of ciphertext, such as letter frequency information of ciphertext [1], [2], entropy analysis of ciphertext [3], [4], and length analysis of ciphertext [5]. In addition to using statistical features, Dileep et al [6] attempted to use text classification to construct dictionaries as features using bag-of-words models.…”

Section: Introductionmentioning

confidence: 99%

Block ciphers identification scheme based on randomness test

Shi²

2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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In real scenarios, cryptanalysts can only obtain ciphertext of unknown cryptographic algorithms to conduct ciphertext only attacks. Cryptosystem identification is a prerequisite for further cryptanalysis. This paper proposes a new method of division for ciphertext sequence based on the four widely used cryptographic algorithms DES, 3DES, AES, and Blowfish, which have high security. We redesigned the ciphertext features using five detection methods in the NIST randomness test standard. In the case of using two working modes (ECB and CBC) of block ciphers, the proposed method in this paper obtains lower feature dimensionality and higher final recognition accuracy compared to some previous research works. This study has certain reference significance for the fast and accurate identification of large volume of ciphertext.

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Cryptosystem Identification Scheme Based on ASCII Code Statistics

Cited by 9 publications

References 14 publications

Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment

Automated Deep Learning BLACK-BOX Attack for Multimedia P-BOX Security Assessment

A Deep Learning Approach for Active S-Box Prediction of Lightweight Generalized Feistel Block Ciphers

Block ciphers identification scheme based on randomness test

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