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Activity Number: 446
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
Sponsor: Quality and Productivity Section
Abstract #311110 View Presentation
Title: Spc Methods for Non-Stationary Correlated Count Data with Applications to Network Surveillance
Author(s): Daniel Jeske*+ and Yingzhuo Fu
Companies: University of California, Riverside and MarketResearch
Keywords: Count Data ; Bartlett Likelihood Ratio ; False Alarm Rate ; Sequential
Abstract:

Network traffic metrics are usually correlated count data that display a non- stationary pattern in their mean structures. We propose to model traffic counts using a generalized linear mixed model to capture these features. We then develop three tracking statistics proposed for anomaly detection. Two of the statistics are derived variants of a Bartlett-type likelihood ratio, which itself is not computationally tractable. The first of these variants is based on an approximation to the integrated likelihood while the second is based on the concept of h-likelihood. We also consider a tracking statistic that is an exponentially weighted moving average. We compare the properties of the three tracking statistics from the point of view of false alarm rate and detection power, and contrast the proposed tracking statistics with current literature.


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