Crowd-based Detection of Routing Anomalies on the Internet

The Internet is highly susceptible to routing attacks and there is no universally deployed solution that ensures that traffic is not hijacked by third parties. Individuals or organizations wanting to protect themselves from sustained attacks must therefore typically rely on measurements and traffic monitoring to detect attacks. Motivated by the high overhead costs of continuous active measurements, we argue that passive monitoring combined with collaborative information sharing and statistics can be used to provide alerts about traffic anomalies that may require further investigation. In this paper we present and evaluate a user-centric crowd-based approach in which users passively monitor their network traffic, share information about potential anomalies, and apply combined collaborative statistics to identify potential routing anomalies. The approach uses only passively collected round-trip time (RTT) measurements, is shown to have low overhead, regardless if a central or distributed architecture is used, and provides an attractive tradeoff between attack detection rates (when there is an attack) and false alert rates (needing further investigation) under normal conditions. Our data-driven analysis using longitudinal and distributed RTT measurements also provides insights into detector selection and the relative weight that should be given to candidate detectors at different distances from the potential victim node