AI-BASED NETWORK MONITORING AND FAULT DETECTION IN MODERN COMPUTER NETWORKS

Abstract

The increasing complexity and scale of modern computer networks have made traditional monitoring and fault detection mechanisms insufficient for ensuring reliable network operation. Conventional rule-based and threshold-driven approaches often fail to detect hidden anomalies and respond promptly to dynamic network conditions. This paper investigates the application of artificial intelligence techniques for network monitoring and fault detection. The study analyzes how machine learning and intelligent data analysis enable proactive detection of abnormal behavior, early identification of network failures, and automated performance optimization. The findings demonstrate that AI-based monitoring systems significantly improve detection accuracy, reduce response time, and enhance overall network reliability. The paper highlights the importance of intelligent and adaptive monitoring frameworks for managing next-generation computer networks.