This book tackles important problems of anomaly detection and health
status analysis in complex core router systems, integral to today's
Internet Protocol (IP) networks. The techniques described provide the
first comprehensive set of data-driven resiliency solutions for core
router systems. The authors present an anomaly detector for core router
systems using correlation-based time series analysis, which monitors a
set of features of a complex core router system. They also describe the
design of a changepoint-based anomaly detector such that anomaly
detection can be adaptive to changes in the statistical features of data
streams. The presentation also includes a symbol-based health status
analyzer that first encodes, as a symbol sequence, the long-term complex
time series collected from a number of core routers, and then utilizes
the symbol sequence for health analysis. Finally, the authors describe
an iterative, self-learning procedure for assessing the health status.
- Enables Accurate Anomaly Detection Using Correlation-Based Time-Series
Analysis;
- Presents the design of a changepoint-based anomaly detector;
- Includes Hierarchical Symbol-based Health-Status Analysis;
- Describes an iterative, self-learning procedure for assessing the
health status.