DocumentCode :
539174
Title :
Maritime anomaly detection and threat assessment
Author :
Lane, R.O. ; Nevell, D.A. ; Hayward, S.D. ; Beaney, T.W.
Author_Institution :
QinetiQ, Malvern, UK
fYear :
2010
fDate :
26-29 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
Ships involved in commercial activities tend to follow set patterns of behaviour depending on the business in which they are engaged. If a ship exhibits anomalous behaviour, this could indicate it is being used for illicit activities. With the wide availability of automatic identification system (AIS) data it is now possible to detect some of these patterns of behaviour. Monitoring the possible threat posed by the worldwide movement of ships, however, requires efficient and robust automatic data processing to create a priority list for further investigation. This paper outlines five anomalous ship behaviours: deviation from standard routes, unexpected AIS activity, unexpected port arrival, close approach, and zone entry. For each behaviour, a process is described for determining the probability that it is anomalous. Individual probabilities are combined using a Bayesian network to calculate the overall probability that a specific threat is present. Examples of how the algorithms work are given using simulated and real data.
Keywords :
Bayes methods; belief networks; marine engineering; security; AIS activity; Bayesian network; anomalous behaviour; anomalous ship behaviour; automatic identification system; individual probability; maritime anomaly detection; robust automatic data processing; threat assessment; unexpected port arrival; Biological system modeling; Hidden Markov models; Marine vehicles; Markov processes; Probability; Sea measurements; Tracking; Automatic identification system (AIS); Bayesian network; anomaly detection; maritime environment; situational awareness; threat assessment; white shipping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
Type :
conf
DOI :
10.1109/ICIF.2010.5711998
Filename :
5711998
Link To Document :
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