Adaptive WAN Link Anomaly Detection Using Lightweight Packet-Level Features for Branch-to-HQ Network Stability

Abstract

Maintaining stable Wide Area Network (WAN) connectivity between branch offices and centralized headquarters infrastructure is essential for the reliable operation of modern enterprise systems. However, WAN links frequently experience performance degradation caused by congestion, routing instability, and intermittent packet loss, which can significantly disrupt enterprise services such as cloud applications, real-time communications, and data synchronization. This study proposes a lightweight anomaly detection framework designed to monitor branch-to-headquarters WAN links using packetlevel telemetry features. The framework utilizes compact statistical indicators derived from packet transmission behaviour, including packet loss ratio, latency deviation, and jitter variance, to characterize network performance conditions. An adaptive anomaly detection model is implemented using a dynamic threshold formulation that adjusts detection boundaries based on the moving average and statistical variance of observed network metrics. The proposed model enables real-time identification of network anomalies while maintaining low computational overhead suitable for deployment on resource-constrained branch routers. Experimental evaluation was conducted using simulated WAN environments representing stable traffic conditions, congestion-induced anomalies, and intermittent packet loss events. The results demonstrate that the lightweight monitoring framework achieves high detection accuracy while maintaining low false-positive rates and reduced detection latency compared with conventional monitoring approaches. The findings indicate that combining packet-level feature engineering with adaptive statistical detection provides an effective and scalable solution for improving WAN stability monitoring in distributed enterprise networks.