Data Encryption Methodologies Enhanced by Hybrid Machine Learning Models for Secured Communication
P. Gomathi, D Shobana, Mariya Princy A · 2025
The rapid advancements in computing technologies, especially quantum computing, pose significant challenges to traditional encryption methods, compelling the need for more robust, adaptive, and scalable solutions. Hybrid machine learning (ML) models have emerged as a promising approach to address these challenges, offering enhanced security, performance, and scalability. This book chapter explores the intersection of hybrid ML models and encryption methodologies, focusing on how these models can transform data encryption techniques for secure communication. By integrating various ML techniques such as supervised, unsupervised, and reinforcement learning, hybrid models provide adaptive encryption strategies that can dynamically respond to emerging threats and evolving system requirements. The chapter delves into the application of hybrid ML models in quantum-safe encryption, key management systems, and real-time adaptive encryption, showcasing case studies that demonstrate their practical impact in securing data in both traditional and quantum computing environments. Through comprehensive analysis, this chapter highlights the potential of hybrid ML models to optimize encryption efficiency, enhance key exchange protocols, and ensure the scalability of encryption systems, paving the way for a secure and future-proof communication infrastructure.