Application of Machine Learning Tools for Implementation and Cryptanalysis of Genetic Encryption Algorithm

Student: Eritsyan Karen

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Information Security (Bachelor)

Final Grade: 8

Year of Graduation: 2024

The paper describes the possibilities of applying machine learning tools to symmetric ciphers, genetic algorithms, an error back propagation method for deep learning, and implements a simple neural network for application to genetic encryption developed and improved by uniform crossover in order to create an identical system and its cryptanalysis based on open texts. A comparative analysis of the time characteristics of the model operation with AES, DES, and RSA ciphers is performed. Experiments were conducted during which the efficiency of the neural network was increased, due to an increase in the number of epochs, the Adam optimization method, changing the size of the training sample, using batch size equal to 16.

Full text (added May 12, 2024)

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