Integrating AI-Based Predictive Analytics in Network Monitoring for Real-Time Fault Detection: A Comprehensive Analysis of Modern Network Infrastructure Management

• Author(s): Sullivan Afanna Ezike
• Paper ID: 1709319
• Page: 307-318
• Published Date: 30-06-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 12 June-2020

Abstract

The exponential growth of network infrastructure complexity in the United States has necessitated the evolution from traditional reactive network monitoring approaches to proactive, AI-driven predictive analytics systems. This paper examines the integration of artificial intelligence and machine learning algorithms in network monitoring frameworks to enable real-time fault detection and prevention. Through comprehensive analysis of current implementations across major U.S. telecommunications providers and enterprise networks, this study demonstrates that AI-based predictive analytics can reduce network downtime by up to 78% and improve fault detection accuracy to 94.3%. The research presents a systematic evaluation of various machine learning algorithms, their effectiveness in different network environments, and provides actionable recommendations for implementation strategies in diverse organizational contexts.

Keywords

Network Monitoring, Predictive Analytics, Artificial Intelligence, Fault Detection, Machine Learning, Network Infrastructure

Citations

IRE Journals:
Sullivan Afanna Ezike "Integrating AI-Based Predictive Analytics in Network Monitoring for Real-Time Fault Detection: A Comprehensive Analysis of Modern Network Infrastructure Management" Iconic Research And Engineering Journals Volume 3 Issue 12 2020 Page 307-318

IEEE:
Sullivan Afanna Ezike "Integrating AI-Based Predictive Analytics in Network Monitoring for Real-Time Fault Detection: A Comprehensive Analysis of Modern Network Infrastructure Management" Iconic Research And Engineering Journals, 3(12)