|Title:||Minerals: Using Data Mining to Detect Router Misconfigurations|
|Authors:||Franck Le, Sihyung Lee, Tina Wong, Hyong S. Kim, Darrell Newcomb|
|Publication Date:||May 23, 2006|
Recent studies have shown that router misconfigurations are pervasive and have dramatic consequences for the operations of networks. Not only can misconfigurations compromise the security of a single network, they can even cause global disruptions in Internet connectivity. Several solutions have been proposed that can detect a number of problems in real configuration files. However, these solutions share a common limitation: they are rule-based. Rules are assumed to be known beforehand, and violations of these rules are deemed misconfigurations. As policies typically differ among networks, rule-based approaches are limited in the scope of mistakes they can detect. In this paper, we address the problem of router misconfigurations using data mining. We apply association rules mining to the configuration files of routers across an administrative domain to discover local, network-specific policies. Deviations from these local policies are potential misconfigurations. We have evaluated our scheme on configuration files from a large state-wide network provider, a large university campus and a high-performance research network, and found promising results. We discovered a number of errors that were confirmed and later corrected by the network engineers. These errors would have been difficult to detect with current rule-based approaches.
Full Report: CMU-CyLab-06-008