Papers Published

1. Jin, S; Zhang, Z; Chakrabarty, K; Gu, X, Changepoint-based anomaly detection in a core router system, Proceedings International Test Conference, vol. 2017-December (December, 2017), pp. 1-10, IEEE [doi] .
   (last updated on 2022/12/30)

   Abstract:
   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-based anomaly detector that first detects changepoints from collected time-series data, and then utilizes these changepoints to detect anomalies. Two approaches based on maximum-likelihood estimation are implemented to detect different types of changepoints. A clustering method is then developed to identify a wide range of normal/abnormal patterns from changepoint windows. Data collected from a set of commercial core router systems are used to validate the proposed anomaly detector. Experimental results show that our changepoint-based anomaly detector achieves better performance than traditional methods.