The Internet is a complex network, comprised of thousands of interconnected Autonomous Systems. Considerable research is done in order to infer the undisclosed commercial relationships between ASes.
These relationships, which have been commonly classified to four distinct Type of Relationships (ToRs), dictate the routing policies between ASes. These policies are a crucial part in understanding the Internet’s traffic and behavior patterns. This work leverages Internet Point of Presence (PoP) level maps to improve AS ToR inference.
We propose a method which uses PoP level maps to find complex AS relationships and detect anomalies on the AS relationship level. We present experimental results of using the method on ToR reported by CAIDA and report several types of anomalies and errors. The results demonstrate the benefits of using PoP level maps for ToR inference, requiring considerable less resources than other methods theoretically capable of detecting similar phenomena.