This article presents a theoretical and experimental investigation of the applicability of deep learning techniques in modern cryptanalysis. Traditional cryptanalytic methods are computationally intensive, as they require detecting complex nonlinear relationships between the key, plaintext, and ciphertext. Deep learning methods, in contrast, operate directly on raw data, independently extract meaningful features, and automatically detect statistical deviations (bias), making them an effective tool for identifying vulnerabilities in cryptographic systems.
| Mualliflar | Rahmatullayev, Ilhom, Abduraximov, Baxtiyor |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-12-23 |
| Son | 4 |
| Betlar | 192-198 |
| Til | Rus |
Deep Learning, Глубокое обучение, Нейронные сети, Криптоанализ, Потоковое шифрование, RC4, RC4A, Trivium, TRIAD, Модель «черного ящика», Keystream, Обнаружение отклонений, Машинное обучение, Свёрточные нейронные сети (CNN), Рекуррентные нейронные сети (RNN), LSTM, Дифференциальный криптоанализ, Анализ побочных каналов, Криптография., Deep Learning, Neural Networks, Cryptanalysis, Stream Cipher, RC4, RC4A, Trivium, TRIAD, Black-Box Model, Keystream, Bias Detection, Machine Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, Differential Cryptanalysis, Side-Channel Analysis, Cryptography., Deep Learning, Chuqur O‘rganish, Neyron Tarmoqlar, Kriptotahlil, Oqimli Shifrlash, RC4, RC4A, Trivium, TRIAD, Black-Box Modeli, Keystream, Bias Aniqlash, Mashinali O‘qitish, Konvolyutsion Neyron Tarmoqlar (CNN), Rekurrent Neyron Tarmoqlar (RNN), LSTM, Differensial Kriptoanaliz, Yon Kanal Tahlili, Kriptografiya.
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