Indexing

Abstract: This study assesses various AI methodologies, such as reinforcement learning and deep neural networks, and their applicability in network optimization for network infrastructures. This study examines how artificial intelligence (AI) can improve a number of network-related tasks, including fault detection, resource allocation, and traffic management. AI-driven models that utilize machine learning (ML) algorithms to predict network behavior, manage traffic in real-time, and optimize resource utilization are gradually replacing traditional network management systems, which frequently rely on static configurations. This study focuses on optimizing resilient and dependable computer networks using a multi-layered architecture, network protocols, network components and elements. This study offers a comparative framework of analysis for network protocols, network components, and the OSI model layered architecture which consists of seven layers. This study uses layered architecture technology to explain the fundamentals of computer networking and offers a structured method of organizing networks and enhances scalability and flexibility.

Keywords: Artificial Intelligence, architecture, computer, components, layers, model, network optimization, protocols, machine learning.

Title: OPTIMIZATION OF NETWORK RESILIENCE AND RELIABILITY USING ARTIFICIAL INTELLIGENCE AND MULTI-LAYERED ARCHITECTURE

Author: Princewill Chinomso Okeke

International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)

ISSN 2349-7815

Vol. 13, Issue 1, January 2026 - March 2026

Page No: 1-14

Paper Publications

Website: www.paperpublications.org

Published Date: 07-February-2026