Алгоритмы использования нейронных сетей для улучшения системы RSA
| Parent link: | Перспективы развития фундаментальных наук=Prospects of Fundamental Sciences Development: сборник научных трудов XХII Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 22-25 апреля 2025/ Национальный исследовательский Томский политехнический университет ; под ред. И. А. Курзиной [и др.].— .— Томск: Изд-во ТПУ Т. 3 : Математика.— 2025.— С. 64-65 |
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| Summary: | Заглавие с экрана This study explores the application of neural network algorithms to improve the security and efficiency of the RSA cryptosystem. With the advent of quantum computing and advanced cryptanalysis techniques, traditional RSA implementations face increasing vulnerabilities. We investigate the use of Generative Adversarial Networks (GANs) for key generation, Long Short-Term Memory (LSTM) networks for encryption optimization, and Convolutional Neural Networks (CNNs) for attack detection. Experimental results demonstrate that GANs enhance key randomness, LSTMs reduce encryption time by 15 %, and CNNs achieve 98 % accuracy in attack detection. The findings highlight the potential of neural networks to reinforce RSA against modern threats while maintaining computational efficiency Текстовый файл |
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2025
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| Online Access: | http://earchive.tpu.ru/handle/11683/133127 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=682687 |