An Anomaly Detection Approach by AIML in IP Networks with eBPF-Based Observability

Highly accurate anomaly detection is indispensable for reliable communication services, and research on anomaly detection with AI/ML has been actively conducted . Detailed system-specific data as input for AI/ML, are essential for autonomous provisioning and failure management. This paper proposes a novel monitoring architecture for measuring detailed system-specific data in routers that comprise transport networks, as an essential element of mobile networks. Previously, vendor-proprietary router implementations were dominant and could only measure limited metrics (e.g., the CPU utilization rate and packet throughput). In recent years, however, the rise of cloud computing and SDNs has led to routers becoming more open and software-oriented. Therefore, we propose a novel eBPF-based monitoring method for routers. The evaluation results obtained by AI/ML show that anomaly detection can be achieved with high accuracy.