NDLI: Anomaly Detection via Feature-Aided Tracking and Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Singh, S. Haiying Tu Donat, W. Pattipati, K. Willett, P.
Copyright Year	1996
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 ldquosignalrdquo observations are far fewer than ldquonoiserdquo observations. Furthermore, the signal observations are ldquohiddenrdquo 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).
Sponsorship	IEEE Systems, Man, and Cybernetics Society
Starting Page	144
Ending Page	159
Page Count	16
File Size	1381583
File Format	PDF
ISSN	10834427
Volume Number	39
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Hidden Markov models Event detection Working environment noise Pattern matching Predictive models Scattering Signal to noise ratio Testing Maximum likelihood detection hidden Markov models Anomaly detection asymmetric threats change detection
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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