Abstract :

Modern Digital Infrastructures Are Becoming Increasingly Complex, Resulting In A Higher Frequency Of Network Incidents That Require Rapid And Accurate Responses. Traditional Manual Incident Management Methods Often Lead To Delays, Human Errors, And Inefficient Resource Utilization. To Overcome These Limitations, This Work Presents A Smart Networking Approach For Automated Incident Management That Integrates Intelligent Monitoring, Machine Learning-based Anomaly Detection, And Automated Decision-making Mechanisms. The System Continuously Analyzes Network Traffic, Correlates Events From Multiple Sources, And Predicts Potential Incidents Using Real-time Analytics. Once An Anomaly Is Detected, An Automation Engine Initiates Context-aware Responses Such As Traffic Rerouting, Node Isolation, Or Alert Generation To Prevent Service Disruption. Experimental Analysis Shows That The Proposed Approach Significantly Reduces Detection Latency, Enhances Accuracy, And Minimizes Downtime, Making It Suitable For Enterprise Networks, Cloud Environments, And Large-scale IoT Systems. Overall, This Smart Networking Framework Offers A Scalable, Proactive, And Efficient Solution For Modern Incident Management. Keywords: Smart Networking, Automated Incident Management, Machine Learning, Anomaly Detection, Event Correlation, Network Automation, Predictive Analytics, Real-Time Monitoring.

Published:

18-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

384-392

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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