The cutting EDGE of IP router configuration
ACM SIGCOMM Computer Communication Review
Detecting BGP configuration faults with static analysis
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
IP network configuration for intradomain traffic engineering
IEEE Network: The Magazine of Global Internetworking
Minerals: using data mining to detect router misconfigurations
Proceedings of the 2006 SIGCOMM workshop on Mining network data
SPIKE: best practice generation for storage area networks
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Detecting and resolving policy misconfigurations in access-control systems
Proceedings of the 13th ACM symposium on Access control models and technologies
Detecting network-wide and router-specific misconfigurations through data mining
IEEE/ACM Transactions on Networking (TON)
Detecting and resolving policy misconfigurations in access-control systems
ACM Transactions on Information and System Security (TISSEC)
On detecting active worms with varying scan rate
Computer Communications
Discovering access-control misconfigurations: new approaches and evaluation methodologies
Proceedings of the second ACM conference on Data and Application Security and Privacy
Efficient recovery from false state in distributed routing algorithms
NETWORKING'10 Proceedings of the 9th IFIP TC 6 international conference on Networking
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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.