Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

The maritime domain is the most utilised environment for bulk transportation, making maritime safety and security an important concern. A major aspect of maritime safety and security is maritime situational awareness. To achieve effective maritime situational awareness, recently many efforts have been made in automatic anomalous maritime vessel movement behaviour detection based on movement data provided by the Automatic Identification System (AIS). In this paper we present a review of state-of-the-art automatic anomalous maritime vessel behaviour detection techniques based on AIS movement data. First, we categorise some approaches proposed in the period 2011 to 2016 to automatically detect anomalous maritime vessel behaviour into distinct categories including statistical, machine learning and data mining, and provide an overview of them. Then we discuss some issues related to the proposed approaches and identify the trend in automatic detection of anomalous maritime vessel behaviour.

[1]  Ke Wang,et al.  Contextual verification for false alarm reduction in maritime anomaly detection , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[2]  P. Silveira,et al.  Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal , 2013, Journal of Navigation.

[3]  Antonio F. Gómez-Skarmeta,et al.  A complex event processing approach to detect abnormal behaviours in the marine environment , 2016, Inf. Syst. Frontiers.

[4]  Maarten van Someren,et al.  Machine learning for vessel trajectories using compression, alignments and domain knowledge , 2012, Expert Syst. Appl..

[5]  Fabio Mazzarella,et al.  Spatio-temporal data mining for maritime situational awareness , 2015, OCEANS 2015 - Genova.

[6]  Niklas Lavesson,et al.  Open data for anomaly detection in maritime surveillance , 2013, Expert Syst. Appl..

[7]  Bengt Carlsson,et al.  Grid Size Optimization for Potential Field based Maritime Anomaly Detection , 2014 .

[8]  G. Shafer,et al.  Algorithmic Learning in a Random World , 2005 .

[9]  Po-Ruey Lei,et al.  A framework for anomaly detection in maritime trajectory behavior , 2015, Knowledge and Information Systems.

[10]  Francesco Palmieri,et al.  Application of Bayesian Techniques to Behavior Analysis in Maritime Environments , 2015, Advances in Neural Networks.

[11]  Ivica Tijardovic The use of AIS for collision avoidance , 2009 .

[12]  Leto Peel,et al.  Maritime anomaly detection using Gaussian Process active learning , 2012, 2012 15th International Conference on Information Fusion.

[13]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[14]  Hans Wehn,et al.  2015 Ieee International Conference on Big Data (big Data) Maritime Situation Analysis Framework Vessel Interaction Classification and Anomaly Detection , 2022 .

[15]  Michele Vespe,et al.  Traffic knowledge discovery from AIS data , 2013, Proceedings of the 16th International Conference on Information Fusion.

[16]  Steven Reece,et al.  Maritime abnormality detection using Gaussian processes , 2013, Knowledge and Information Systems.

[17]  Jean Roy,et al.  Anomaly detection in the maritime domain , 2008, SPIE Defense + Commercial Sensing.

[18]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[19]  Steven Reece,et al.  Online Maritime Abnormality Detection Using Gaussian Processes and Extreme Value Theory , 2012, 2012 IEEE 12th International Conference on Data Mining.

[20]  F. J. Anscombe,et al.  Rejection of Outliers , 1960 .

[21]  Luca Cazzanti,et al.  A document-based data model for large scale computational maritime situational awareness , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[22]  Stan Matwin,et al.  Vessel route anomaly detection with Hadoop MapReduce , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[23]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[24]  Jurgen Beyerer,et al.  Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain , 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

[25]  Kevin B. Korb,et al.  Anomaly detection in vessel tracks using Bayesian networks , 2014, Int. J. Approx. Reason..

[26]  Hans Wehn,et al.  Maritime Situation Analysis: A Multi-vessel Interaction and Anomaly Detection Framework , 2014, 2014 IEEE Joint Intelligence and Security Informatics Conference.

[27]  Lars Linsen,et al.  Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction , 2014 .

[28]  Anne-Laure Jousselme,et al.  Data-driven detection and context-based classification of maritime anomalies , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[29]  Stan Matwin,et al.  Anomaly detection in maritime data based on geometrical analysis of trajectories , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[30]  John C. McEachen,et al.  Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness , 2014, 17th International Conference on Information Fusion (FUSION).

[31]  Yasuhiro Nakamura,et al.  Predicting Ship Behavior Navigating through Heavily Trafficked Fairways by Analyzing AIS Data on Apache HBase , 2013, 2013 First International Symposium on Computing and Networking.

[32]  Göran Falkman,et al.  Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories , 2013, Annals of Mathematics and Artificial Intelligence.

[33]  Stan Matwin,et al.  Knowledge-based clustering of ship trajectories using density-based approach , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[34]  Richard O. Lane,et al.  Maritime anomaly detection and threat assessment , 2010, 2010 13th International Conference on Information Fusion.

[35]  Miroslav Voznák,et al.  Self-learning adaptive algorithm for maritime traffic abnormal movement detection based on virtual pheromone method , 2015, 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS).

[36]  Xavier Lerouvreur,et al.  Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness , 2013, Proceedings of the 16th International Conference on Information Fusion.

[37]  Julita Vassileva,et al.  Collaboration technology in teams and organizations: Introduction to the special issue , 2016, Inf. Syst. Frontiers.

[38]  Göran Falkman,et al.  Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance , 2011, 14th International Conference on Information Fusion.

[39]  Simone Bassis,et al.  Advances in Neural Networks: Computational and Theoretical Issues , 2015, Smart Innovation, Systems and Technologies.

[40]  Stan Matwin,et al.  Ship movement anomaly detection using specialized distance measures , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[41]  Wil M. P. van der Aalst,et al.  Analyzing Vessel Behavior Using Process Mining , 2013, Situation Awareness with Systems of Systems.

[42]  Aldo Napoli,et al.  A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behavior analysis , 2013 .

[43]  Felix Naumann,et al.  Data fusion , 2009, CSUR.