Anomaly detection via feature-aided tracking and hidden Markov models

Authors:
Satnam Singh;Haiying Tu;William Donat;Krishna Pattipati;Peter Willett
Affiliations:
General Motors India Science Laboratory, Bangalore, India;The MathWorks, Natick, MA;Pratt & Whitney, East Hartford, CT;Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT;Electrical and Computer Engineering Department, University of Connecticut, Storrs, CT
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Year:
2009

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Abstract

The problem of detecting an anomaly (or abnormal event) is such that the distribution of observations is different before and after an unknown onset time, and the objective is to detect the change by statistically matching the observed pattern with that predicted by a model. In the context of asymmetric threats, the detection of an abnormal situation refers to the discovery of suspicious activities of a hostile nation or group out of noisy, scattered, and partial intelligence data. The problem becomes complex in a low signal-to-noise ratio environment, such as asymmetric threats, because the "signal" observations are far fewer than "noise" observations. Furthermore, the signal observations are "hidden" in the noise. In this paper, we illustrate the capabilities of hidden Markov models (HMMs), combined with feature-aided tracking, for the detection of asymmetric threats. A transaction-based probabilistic model is proposed to combine HMMs and feature-aided tracking. A procedure analogous to Page's test is used for the quickest detection of abnormal events. The simulation results show that our method is able to detect the modeled pattern of an asymmetric threat with a high performance as compared to a maximum likelihood-based data mining technique. Performance analysis shows that the detection of HMMs improves with increase in the complexity of HMMs (i.e., the number of states in an HMM).