Pathcache: A Path Prediction Toolkit

Path prediction on the Internet has been a topic of research in the networking community for close to a decade. Applications of path prediction solutions have ranged from optimizing selection of peers in peer- to-peer networks to improving and debugging CDN predictions. Recently, revelations of traffic correlation and surveillance on the Internet have raised the topic of path prediction in the context of network security. Specifically, predicting network paths can allow us to identify and avoid given organizations on network paths (e.g., to avoid traffic correlation attacks in Tor) or to infer the impact of hijacks and interceptions when direct measurements are not available. In this poster we propose the design and implementation of PathCache which aims to reuse measurement data to estimate AS level paths on the Internet. Unlike similar systems, PathCache does not assume that routing on the Internet is destination based. Instead, we develop an algorithm to compute confidence in paths between ASes. These multiple paths ranked by their confidence values are returned to the user.