Titlebook: Anomaly-Detection and Health-Analysis Techniques for Core Router Systems; Shi Jin,Zhaobo Zhang,Xinli Gu Book 2020 Springer Nature Switzerl

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Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis,rrelation analyzer is implemented to remove irrelevant and redundant features. Three types of synthetic anomalies, generated using a small amount of real data for a commercial telecom system, are used to validate the proposed anomaly detector.

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Includes Hierarchical Symbol-based Health-Status Analysis.DeThis 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-d

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https://doi.org/10.1007/978-3-8348-9949-1ing more difficult to detect, diagnose and repair in time. Next, we discuss various prior work on anomaly detection, including their advantages and disadvantages. Finally, we summarize current technique challenges and the overall motivation of this book.

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Introduction,ing more difficult to detect, diagnose and repair in time. Next, we discuss various prior work on anomaly detection, including their advantages and disadvantages. Finally, we summarize current technique challenges and the overall motivation of this book.

Flatter

https://doi.org/10.1007/978-3-8348-9949-1s is then described, including both hardware and software advancement. A wide range of faults in routers are also discussed to show why they are becoming more difficult to detect, diagnose and repair in time. Next, we discuss various prior work on anomaly detection, including their advantages and di

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https://doi.org/10.1007/BFb0046097nt mechanism depends on whether anomalies can be accurately detected before a failure occurs. In this chapter, we present an accurate anomaly detector for core router systems using correlation-based time-series analysis. The proposed method monitors the time-series data of a complex core router syst

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https://doi.org/10.1007/BFb0046097prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect ”outliers” when the statistical properties of the monitored data change significantly as time proceeds. In this chapter, we describe

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correspondent

https://doi.org/10.1007/BFb0046097rge amount operational data is collected from core routers, due to high computational complexity and expensive labor cost, only a small part of this data is labeled by experts. The lack of labels is an impediment towards the adoption of supervised learning. We present an iterative self-learning proc

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