Abstract
As machine learning is used to analyse data and produce some action based on that data, the application in the domain of cryptanalysis opens new points of view, since the key space of any complex cipher system is large. When concerning machine learning with the algorithms of cryptography, the possibility to learn from the experience that is provided by information retrieval, is being considered. With the understanding that cryptanalysis can be used for improvement and producing more complex solutions in the field of cryptography, common features are sought between these three fields. This paper aims to present the application of machine learning in the field of cryptanalysis, as a prime step for auditing and checking the strength of cryptosystems. Some of these cryptosystems ensure confidentiality and security of a variety of information exchanged from source to destination using symmetric key cryptography. Accordingly, understanding how the algorithms are maintaining the stability is the fundamental aspect for proceeding with the analysis. The experimental part of this paper provides a solution for application of neural networks in cryptanalysis. We show that AES with ECB mode is vulnerable to ciphertext-plaintext attacks. The analysis is focused on trying different sizes of keys for obtaining the ciphertexts from plaintexts. At the end, we discuss how that is impacting the difficulty of data reveal by decrypting with DNN (Artificial Neural Network).
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Gjorgjievska Perusheska, M., Dimitrova, V., Popovska-Mitrovikj, A., Andonov, S. (2021). Application of Machine Learning in Cryptanalysis Concerning Algorithms from Symmetric Cryptography. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_59
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