Bayesian detection of router configuration anomalies

  • Authors:
  • Khalid El-Arini;Kevin Killourhy

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
  • Year:
  • 2005

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Abstract

Problems arising from router misconfigurations cost time and money. The first step in fixing such misconfigurations is finding them. In this paper, we propose a method for detecting misconfigurations that does not depend on an a priori model of what constitutes a correct configuration. Our hypothesis is that uncommon or unexpected misconfigurations in router data can be identified as statistical anomalies within a Bayesian framework. We present a detection algorithm based on this framework, and show that it is able to detect errors in the router configuration files of a university network.