Bayesian detection of router configuration anomalies

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.