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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

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

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

Authors

  • Davlatov Mirzo-Ulugbek
    Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan
  • Allanov Orif
    Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan
  • Turdibekov Baxtiyor
    Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Uzbekistan

DOI:

https://doi.org/10.37547/ajast/Volume05Issue12-04

Keywords:

Cryptanalysis Vigenere cipher Artificial neural networks Deep learning

Abstract

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.

References

Sinkov, A. Elementary Cryptanalysis: A Mathematical Approach. Mathematical Association of America, 1966.

Millichap, C., & Yau, Y. (2023). An artificial neural network approach to finding the key length of the Vigenère cipher. arXiv preprint arXiv:2312.09956.

Greydanus, S. (2017). Learning to Decrypt Classical Ciphers with Recurrent Neural Networks. arXiv preprint arXiv:1708.07576.

Focardi, R., & Luccio, F. L. (2016). Neural Cryptanalysis of Classical Ciphers. CEUR Workshop Proceedings.

Jeong, O. (2024). Comprehensive Neural Cryptanalysis on Block Ciphers (DES, AES, SPECK). Mathematics, 12(13), 1936.

Abd, A. (2018). Classification and Identification of Classical Cipher Type Using Artificial Neural Networks. Journal of Information & Systems Management, 8(3), 94–104.

Xiao, Y., Hao, Q., & Yao, D. (2019). Neural Cryptanalysis: Metrics, Methodology, and Applications in CPS Ciphers. Proceedings, Neural Cryptanalysis Workshop.

Abadi, M., & Andersen, D. G. (2016). Learning to Protect Communications with Adversarial Neural Cryptography.

Greydanus, S. (2017). Learning the Enigma with Recurrent Neural Networks.

Bhattacharyya, S., & Ghosh, S. (2018). Application of Deep Learning in Classical Cipher Cryptanalysis.