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Anomaly Detection

Nadipuram R. Prasad1, Salvador Almanza-Garcia1, Thomas T. Lu2
New Mexico State University, Las Cruces, NM 88003
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109

Computers, Materials & Continua 2009, 14(1), 1-22. https://doi.org/10.3970/cmc.2009.014.001

Abstract

The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly following its detection. Substantial investigative time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to such abnormal behavior. The need to automatically detect anomalous behavior is therefore critical using principles of state motion, and to do so with a human operator in the loop. Human-machine interaction results in a capability for machine self-learning and in producing a robust decision-support mechanism. This is the fundamental concept of intelligent control wherein machine-learning is enhanced by interaction with human operators.

Keywords

Anomaly detection, soft-computing, decision-making, machine intelligence, nonlinear dynamical systems

Cite This Article

N. R. . Prasad, S. . Almanza-Garcia and T. T. . Lu, "Anomaly detection," Computers, Materials & Continua, vol. 14, no.1, pp. 1–22, 2009.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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