Anomaly detection: A Survey. Chandola, V., Banerjee, A., & Kumar, V. ACM Computing Surveys, 41(3):1–58, jul, 2009. ![Anomaly detection: A Survey [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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. Copyright © 2009 Tech Science Press.
@Article{ chandola.ea2009-anomaly,
author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
year = {2009},
title = {Anomaly detection: A Survey},
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. Copyright
{\textcopyright} 2009 Tech Science Press.},
doi = {10.1145/1541880.1541882},
issn = {0360-0300},
journal = {ACM Computing Surveys},
keywords = {Anomaly detection,Decision-making,Machine
intelligence,Nonlinear dynamical
systems,Soft-computing,statistics},
mendeley-tags= {statistics},
month = {jul},
number = {3},
pages = {1--58},
url = {https://dl.acm.org/doi/10.1145/1541880.1541882},
volume = {41}
}
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