Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Nonparametric cusum algorithms with applications to network surveillance
Nonparametric cusum algorithms with applications to network surveillance
Understanding and evaluating the impact of sampling on anomaly detection techniques
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Statistical analysis of network traffic for adaptive faults detection
IEEE Transactions on Neural Networks
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We develop two cusum change-point detection algorithms for data network monitoring applications where numerous and various performance and reliability metrics are available to aid with the early identification of realized or impending failures. We confront three significant challenges with our cusum algorithms: (1) the need for nonparametric techniques so that a wide variety of metrics can be included in the monitoring process, (2) the need to handle time varying distributions for the metrics that reflect natural cycles in work load and traffic patterns, and (3) the need to be computationally efficient with the massive amounts of data that are available for processing. The only critical assumption we make when developing the algorithms is that suitably transformed observations within a defined timeslot structure are independent and identically distributed under normal operating conditions. To facilitate practical implementations of the algorithms, we present asymptotically valid thresholds. Our research was motivated by a real-world application and we use that context to guide the design of a simulation study that examines the sensitivity of the cusum algorithms.