NetMoniAI: An Agentic AI Framework for Network Security & Monitoring
This addresses network security for practitioners and researchers, offering an incremental improvement through a two-tier design.
The paper tackles network security and monitoring by introducing NetMoniAI, an agentic AI framework that uses decentralized analysis with centralized coordination, resulting in improved scalability, reduced redundancy, and faster response times without accuracy loss.
In this paper, we present NetMoniAI, an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination. The framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection. A central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness. We evaluated NetMoniAI on a local micro-testbed and through NS-3 simulations. Results confirm that the two-tier agentic-AI design scales under resource constraints, reduces redundancy, and improves response time without compromising accuracy. To facilitate broader adoption and reproducibility, the complete framework is available as open source. This enables researchers and practitioners to replicate, validate, and extend it across diverse network environments and threat scenarios. Github link: https://github.com/pzambare3/NetMoniAI