Rademics Research Institute

Abstract

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. 

Introduction

The rapid expansion of the Internet of Things (IoT) and the increasing reliance on edge computing have transformed the landscape of digital communication and data processing [1]. As more devices become interconnected, the volume of data exchanged across networks continues to grow exponentially [2]. These advancements, while offering numerous benefits in terms of efficiency and accessibility, also introduce significant security risks [3]. Traditional cryptographic systems, while effective in many use cases, are often inadequate when applied to the unique challenges posed by IoT and edge environments [4]. In particular, resource constraints, such as limited processing power, storage, and energy supply in IoT devices, complicate the implementation of robust encryption schemes [5]. As cyber threats evolve and become more sophisticated, the need for adaptive cryptographic systems that can handle these challenges is more pressing than ever [6].

To address these challenges, recent research has explored the integration of machine learning techniques, specifically neural network ensembles, with statistical models to create more robust encryption solutions [7]. Neural network ensembles consist of multiple individual neural networks working together to enhance prediction accuracy and system performance [8]. By leveraging their ability to learn from large datasets, neural network ensembles can detect complex patterns in data, offering the potential to identify threats and vulnerabilities in real-time [9]. On the other hand, statistical models provide a deeper understanding of data distributions and statistical relationships, making them particularly valuable for improving key generation, encryption strength, and threat detection capabilities [10]. The integration of these two powerful approaches offers the potential to enhance both the security and efficiency of cryptographic systems [11].