Ai-Driven Encryption Techniques for Secure Communication

Abstract

Methods for safeguarding data transfer in telecommunications, responding to the growing need for stringent cybersecurity protocols amid rising cyber threats and data breaches. Conventional encryption techniques, while successful, can exhibit computational inefficiencies, susceptibility to advancing attacks, and difficulties in key management. This study introduces an adaptive encryption system that utilizes artificial intelligence, namely machine learning and deep learning methods, to dynamically improve security measures while maximizing computing efficiency. The suggested AI-based encryption strategy incorporates predictive analytics for threat identification, automated key generation, and sophisticated encryption techniques to enhance data security against unwanted access and cyberattacks. Experimental findings indicate substantial enhancements in encryption velocity, data integrity, and resistance to diverse cryptographic assaults, alongside a decrease in computing overhead and energy use. The research emphasizes the flexibility of AI-driven encryption in addressing new cybersecurity concerns, facilitating safe, real-time communication in telecommunications networks. The results highlight the capability of AI to transform cryptographic methods, providing a scalable, efficient, and intelligent security framework for contemporary telecommunications systems. Future research must concentrate on improving AI-driven encryption methods via the incorporation of blockchain, federated learning, and hybrid cryptographic frameworks to augment security, privacy, and efficiency in data transmission.