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 oil 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.
CITATION STYLE
Bengtsson, T., Salamon, T., Ho, T. K., & White, C. A. (2010). Model-Based Anomaly Detection for a Transparent Optical Transmission System (pp. 263–286). https://doi.org/10.1007/978-1-84882-765-3_12
Mendeley helps you to discover research relevant for your work.