Abstract
In this chapter, we present an approach for anomaly detection at the physical layer of networks where detailed knowledge about the devices and their operations is available. The approach combines physics-based process models with observational data models to characterize the uncertainties and derive the alarm decision rules. We formulate and apply three different methods based on this approach for a well-defined problem in optical network monitoring that features many typical challenges for this methodology. Specifically, we address the problem of monitoring optically transparent transmission systems that use dynamically controlled Raman amplification systems. We use models of amplifier physics together with statistical estimation to derive alarm decision rules and use these rules to automatically discriminate between measurement errors, anomalous losses, and pump failures. Our approach has led to an efficient tool for systematically detecting anomalies in the system behavior of a deployed network, where pro-active measures to address such anomalies are key to preventing unnecessary disturbances to the system’s continuous operation.
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Acknowledgements
We thank Wonsuck Lee, Lawrence Cowsar, and Roland Freund for early discussions on possible approaches. In identifying useful diagnostic data we were advised by many members of Alcatel-Lucent’s Optical Networking Group. Special thanks go to Steve Eichblatt, Sydney Taegar, and Lee Vallone who conceived and developed the transmission diagnostic data retrieval tool. Narasimhan Raghavan, Bill Thompson, Jeff Sutton, and Tom Kissell provided much needed insight into the system engineering, operation, and testing issues. Their help is gratefully acknowledged.
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Bengtsson, T., Salamon, T., Ho, T.K., White, C.A. (2010). Model-Based Anomaly Detection for a Transparent Optical Transmission System. In: Cormode, G., Thottan, M. (eds) Algorithms for Next Generation Networks. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-765-3_12
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DOI: https://doi.org/10.1007/978-1-84882-765-3_12
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