用智能代理框架实现高效分布式网络监控与安全防护
NetMoniAI: An Agentic AI Framework for Network Security & Monitoring
- 节点部署微智能体进行本地流量分析，中心控制器协同全局洞察
- 在资源受限下仍保持低冗余、快响应，准确识别协同攻击
- 开源可复现，适合网络安全研究者与运维人员快速部署
本文提出NetMoniAI，一种融合去中心化分析与轻量级集中协调的智能代理式网络监控与安全框架。系统分两层：各节点部署自主微代理执行本地流量分析与异常检测；中心控制器聚合多节点信息，识别协同攻击并维持全局态势感知。我们在本地微型实验平台及NS-3仿真环境中评估该框架，结果表明其两级智能代理架构在资源受限条件下具备良好可扩展性，有效降低冗余，提升响应速度且不牺牲准确性。为促进广泛采用与可复现性，完整框架已开源。研究人员与从业者可通过该开源项目在多样化网络环境与威胁场景中复制、验证与拓展本方案。Github链接：https://github.com/pzambare3/NetMoniAI
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。