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Anomaly-Detection and Health-Analysis Techniques for Core Router Systems [electronic resource] / by Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu.

By: Jin, Shi [author.].
Contributor(s): Zhang, Zhaobo [author.] | Chakrabarty, Krishnendu [author.] | Gu, Xinli [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIII, 148 p. 101 illus., 90 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030336646.Subject(s): Electronic circuits | Cooperating objects (Computer systems) | Telecommunication | Electronic Circuits and Systems | Cyber-Physical Systems | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
Contents:
Introduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion.
In: Springer Nature eBookSummary: 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.
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Introduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion.

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.

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