Changepoint-Based Anomaly Detection for Prognostic Diagnosis in a Core Router System

Published

Journal Article

© 2018 IEEE. Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic 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. We describe the design of a changepoint (CP)-based anomaly detector that first detects CPs from collected time-series data, and then utilizes these CPs to detect anomalies. Different CP detection approaches are implemented to detect various types of CPs. A clustering method is then developed to identify normal/abnormal patterns from CP windows. Data collected from a set of commercial core router systems are used to validate the proposed anomaly detector. Experimental results show that our CP-based anomaly detector achieves better performance than traditional methods in terms of two metrics, namely success ratio and nonfalse-alarm ratio.

Full Text

Duke Authors

Cited Authors

  • Jin, S; Zhang, Z; Chakrabarty, K; Gu, X

Published Date

  • July 1, 2019

Published In

Volume / Issue

  • 38 / 7

Start / End Page

  • 1331 - 1344

International Standard Serial Number (ISSN)

  • 0278-0070

Digital Object Identifier (DOI)

  • 10.1109/TCAD.2018.2846641

Citation Source

  • Scopus