Enhancing Cryptographic Security with Deep Learning: Intelligent Threat Detection and Attack Prevention

Abstract

Consistent encrypted security shields important information through contemporary cybersecurity systems by blocking unauthorized users and their attempts to attack. Two main issues arise from traditional security methods when dealing with modern cryptographic threats so artificial intelligence (AI)-driven solutions should integrate to address them. This research examines how deep learning models specifically CNN and LSTM together with GAN operate to detect and prevent threats in cryptographic environments. The proposed method encompassed extracting features from encrypted data followed by picking a model and classifying potential attacks. Deep learning models demonstrated exceptional performance in detection capabilities over standard security mechanisms because the CNN provided the best results for identifying cryptographic anomalies. Deep learning models face two primary challenges for cryptographic integration which consist of extensive computational needs combined with vulnerability to hostile interferences. Statistical tests with ANOVA enforced that deep learning-based security frameworks perform best in strengthening cryptographic security due to their demonstrated proven effectiveness. Research findings prove that deep learning technology improves cryptographic protection through better identification of attacks and better identification accuracy of threats.

Keywords: Adversarial attacks, Artificial intelligence, Cryptographic security, cybersecurity, Deep learning,  Machine learning