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Deep Learning-Based Cryptanalysis of a Simplified AES Cipher

Deep Learning-Based Cryptanalysis of a Simplified AES Cipher

Hicham Grari (Chouaib Doukkali University, Morocco), Khalid Zine-Dine (Faculty of Sciences, Mohammed V University in Rabat, El Jadida, El Jadida, Morocco), Khalid Zine-Dine (Faculty of Sciences, Mohammed V University in Rabat, Morocco), Ahmed Azouaoui (Chouaib Doukkali University, Morocco), and Siham Lamzabi (Laboratory of Innovation in Management and Engineering for Entreprise (LIMIE), ISGA Rabat, Morocco)
Copyright: © 2022 |Pages: 16
DOI: 10.4018/IJISP.300325
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Abstract

Recently, Deep Neural Networks have shown great deal of reliability and applicability as its applications spread in different areas. This paper proposes a cryptanalysis model based on Deep Neural Network, the neural network takes in plaintexts and their corresponding ciphertexts to predict the secret key of the cipher. We proposes two different approaches, in the first we use multi-layer perceptron (MLP). While in the second, the cryptanalysis problem is modeled as a multi-label classification problem, we introduce appropriate Deep Neural Network based methods for tackling such problem. We illustrate the effectiveness of the approach of the DNN-based cryptanalysis by attacking on Simplified AES block cipher. Therefore, specific metrics are readapted to the cryptanalysis context and used to evaluate the proposed schemes. The results indicate that treating cryptanalysis problem as multi-label classification is more suitable and can be a useful and promising tool for the cryptanalysis task.
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Using ML techniques in cryptanalysis filed was subject of several researches. However, Rivest was the first to make a relationship between the fields of cryptography and ML with an emphasis on how each field has contributed ideas and techniques to the other (Rivest, 1991). Bafghi et al. developed a model based on recurrent neural network to represent the differential operation of block ciphers in order to help finding differential characteristics (Bafghi et al., 2008), they improved the optimization results using Boltzmann machine with simulated annealing. Then, Focardi and Luccio applied an artificial neural network to automate a part of the cryptanalytic attack on the classical ciphers such Caesar, Vigenère and substitution cipher, by exploiting a known statistical weakness based on the letters frequencies in a language (Focardi & Luccio, 2018).

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