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.
| Mualliflar | Давлатов, Мирзо-Улугбек, Алланов, Ориф, Турдибеков, Бахтиёр |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-10-03 |
| Son | 3 |
| Betlar | 68-74 |
| Til | Rus |
криптоанализ, шифр Виженера, искусственные нейронные сети, глубокое обучение, рекуррентная нейронная сеть (RNN), архитектура Transformer, криптография, машинное обучение, определение длины ключа, алгоритм дешифрования., kriptotahlil, Vijener shifri, Sun’iy neyron tarmoqlar, Chuqur o‘rganish, Recurrent Neural Network (RNN), Transformer, Kriptografiya, Mashinaviy o‘rganish, Kalit uzunligini aniqlash, Deshifrlash algoritmi
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