In an era marked by increasing cyber threats and data breaches, the efficiency and robustness of cryptographic algorithms have become paramount to ensuring digital security. Traditional cryptographic methods, while secure, often suffer from computational inefficiencies and are vulnerable to emerging attack vectors, especially in resource-constrained environments such as IoT and mobile networks. This study aims to explore and optimize cryptographic algorithms using deep learning techniques to enhance both security and performance in cyber defense applications.

The primary objective is to leverage artificial neural networks (ANNs) to analyze and improve the efficiency, adaptability, and resilience of popular cryptographic algorithms such as AES, RSA, and ECC. A deep learning framework is proposed to identify patterns in encryption-decryption processes and optimize key generation, encryption time, and resistance to attacks. The methodology involves training neural networks on large datasets of encrypted traffic and assessing performance using key metrics including execution time, accuracy, and entropy.

Experimental results demonstrate that integrating deep learning models with conventional cryptographic schemes can significantly reduce computational overhead while maintaining or improving security standards. The findings suggest a promising direction for the development of intelligent, adaptive, and sustainable cryptographic systems suited for real-time cyber security applications. This research contributes to the growing field of AI-based cyber security by offering a novel, optimization-focused approach to modern cryptography.