AI-Powered Adaptive Systems for Fault Detection in Distributed Networks
L. Bhagyalakshmi, G Lingeswaran, P. Madhavi, J Supriya, G Deepa, Sanjay Kumar Suman
Abstract
In distributed networks, ensuring high reliability and minimal downtime is critical, making efficient fault detection a top priority. Traditional fault detection methods often struggle with scalability, real-time responsiveness, and adaptability to changing network conditions. This paper presents an AI-powered adaptive system designed to enhance fault detection in distributed networks. Leveraging machine learning algorithms, our system continuously learns from network data, dynamically adjusting its fault detection strategies to improve accuracy and reduce false positives. Experimental results demonstrate significant improvements in detection accuracy, responsiveness, and overall network reliability. This research highlights the potential of integrating AI to create more resilient and intelligent distributed network systems.