Real Time Detection of Link Failures in Inter Domain Routing

Measurements have shown that network path failures occur frequently in the Internet and physical link failures can cause network instability in large scale and severity. Thus early anomaly detection mechanisms are of great importance. In this paper, we propose a Bayesian approach for time efficient link failure detection using border gateway protocol (BGP) update message traces. The detection is done using an automated mechanism to label, train and classify the network status based on features extracted from BGP traces. In addition to detecting temporal changes in these features, our scheme augments its accuracy by including information on the spatial correlation of the route updates in the decision process. We validate our approach by testing the proposed mechanism on real BGP traces collected during three typical network outage events caused by link failures.