Application Of Neural Networks In Cryptanalysis: The Case Of The Vigenère Algorithm

This paper investigates the application of neural networks in cryptanalysis processes using the Vigenere cipher as a case study. Although the Vigenere cipher, one of the classical polyalphabetic substitution algorithms, was historically regarded as a strong cryptosystem, modern computational capabilities and artificial intelligence approaches help reveal its weaknesses. In this study, a neural network model was built using the PyTorch library, and experiments were conducted to reconstruct plaintext from ciphertext. The experimental results demonstrated that neural networks can learn statistical patterns inherent in the Vigenere cipher and are capable of partially automating the decryption process. This work highlights the potential of neural networks as a complement to classical cryptanalysis methods and proposes new approaches for evaluating the robustness of cryptosystems.

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