Neural Network Ensembles Combined with Statistical Models for Enhanced Encryption Algorithms
D Shobana, W. Nancy, P.R. Therasa · 2025
The rapid evolution of cryptographic systems has driven the need for innovative approaches to enhance data security, particularly in the context of modern technological landscapes such as Internet of Things (IoT) and edge computing environments. This chapter explores the synergistic integration of neural network ensembles and predictive statistical models, offering a promising paradigm for strengthening cryptographic solutions. Neural network ensembles, known for their pattern recognition capabilities, provide an adaptive layer of security, while statistical models offer robust analytical insights that improve encryption robustness. Together, these technologies address the growing demands for secure, efficient, and scalable encryption in resource-constrained environments. Special attention is given to the optimization algorithms required to harmonize these approaches, as well as their practical applications in real-world cryptographic systems. Additionally, the chapter examines the critical role of entropy analysis in fortifying key robustness and the challenges posed by distributed, dynamic networks in IoT and edge computing. The integration of these methodologies aims to bridge the gap between traditional cryptographic techniques and emerging security needs, paving the way for more resilient, adaptive encryption systems.