Anomalous interdomain Border Gateway Protocol (BGP) events including misconfigurations, attacks and large-scale power failures often affect the global routing infrastructure. Thus, the ability to detect and categorize such events is extremely useful. In this article we present a novel anomaly detection technique for BGP that distinguishes between different anomalies in BGP traffic. This technique is termed Higher Order Path Analysis (HOPA) and focuses on the discovery of patterns in higher order paths in supervised learning datasets. Our results demonstrate that not only worm events but also different types of worms as well as blackout events are cleanly separable and can be classified in real time based on our incremental approach. This novel approach to supervised learning has potential applications in cybersecurity/forensics and text/data mining in general.