NetMoniAI: An Agentic AI Framework for Network Security & Monitoring

The system demonstrated scalable, distributed threat detection, dynamic role classification, and responsive semantic analysis. Particularly, it achieved these capabilities without introducing processing bottlenecks or significant latency overhead. C. Conclusion This paper presented NetMoniAI, a hybrid agentic AI framework for real-time, distributed network monitoring and threat detection. By combining decentralized sensing at node level with centralized semantic analysis using GPT -O3, the system detects both localized and coordinated attacks with low latency and high accuracy. Evaluated across a local micro-testbed and NS-3 simulations, NetMoniAI demonstrated timely anomaly detection, accurate DDoS classification, and clear operator feedback through structured reports and an interactive dashboard. Its scalable, asynchronous architecture supports interpretable, layered responses without sacrificing performance. Future work will extend the framework with adaptive mitigation, multi-agent coordination, and SDN-based policy enforcement.