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Author Long, Xiaobo ♦ Sikdar, Biplab
Source CiteSeerX
Content type Text
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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Abstract — 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. Inter domain routing protocols like Border Gateway Protocol (BGP) can take up to 15 minutes to converge after such failures and during the convergence period, packets may encounter transient loops, delays and losses [1]. Thus early anomaly detection mechanisms are of great importance. In this paper, we propose a Bayesian approach for time efficient link failure detection using 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. I.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study