Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T16:44:01.107Z Has data issue: false hasContentIssue false
  • English
  • Français

Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

Published online by Cambridge University Press:  08 March 2017

Abdoulaye Sidibé  and
Gao Shu
Abdoulaye Sidibé*
Affiliation:
(School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China)
Gao Shu
Affiliation:
(School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430063, China) (Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology, Wuhan 430063, China)
*
(E-mail: abdoul@bk.ru)
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

Anomalous vessel behaviourAutomatic Identification System (AIS)Vessel movement behaviour
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 4 , July 2017 , pp. 847 - 858
Copyright
Copyright © The Royal Institute of Navigation 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anneken, M., Fischer, Y. and Beyerer, J. (2015). Evaluation and comparison of anomaly detection algorithms in annotated datasets from the maritime domain. Proceedings of IEEE SAI Intelligent Systems Conference (IntelliSys), 169–178.CrossRefGoogle Scholar
Anscombe, F.J. and Guttman, I. (1960). Rejection of outliers. Technometrics, 2(2), 123–147.CrossRefGoogle Scholar
Arguedas, V.F., Mazzarella, F. and Vespe, M. (2015). Spatio-temporal data mining for maritime situational awareness. Proceedings of the IEEE Conference OCEANS 2015-Genova, 1–8.Google Scholar
Brax, N., Andonoff, E. and Gleizes, M.-P. (2012). A Self-adaptive Multi-Agent System for Abnormal Behavior Detection in Maritime Surveillance. In: Jezic, G. et al. (Eds.), KES-AMSTA 2012, LNAI. Springer-Verlag Inc., 174–185.Google Scholar
Castaldo, F., Palmieri, F.A.N. and Regazzoni, C. (2015). Application of Bayesian Techniques to Behavior Analysis in Maritime Environments. In: Bassis, S., Esposito, A., Morabito, F.C. (eds.), Advances in Neural Networks: Computational and Theoretical Issues. Springer International Publishing Inc., Switzerland, 175–183, DOI: 10.1007/978-3-319-18164-6_17.CrossRefGoogle Scholar
Cazzanti, L., Millefiori, L.M. and Arcieri, G. (2015). A Document-based Data Model for Large Scale Computational Maritime Situational Awareness. Proceedings of the IEEE International Conference on Big Data (Big Data), 1350–1356.CrossRefGoogle Scholar
Chandola, V., Banerjee, A. and Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.CrossRefGoogle Scholar
De Vries, G.K.D. and Van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications, 39(18), 13426–13439.CrossRefGoogle Scholar
Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference Knowledge Discovery and Data Mining (KDD'96), 226–231.Google Scholar
Guillarme, N.L. and Lerouvreur, X. (2013). Unsupervised extraction of knowledge from S-AIS data for maritime situational awareness. Proceedings of the 16th IEEE International Conference on Information Fusion (FUSION), 2025–2032.Google Scholar
Kazemi, S., Abghari, S., Lavesson, N., Johnson, H. and Ryman, P. (2013). Open data for anomaly detection in maritime surveillance. Expert Systems with Applications, 40(14), 5719–5729.CrossRefGoogle Scholar
Kowalska, K. and Peel, L. (2012). Maritime Anomaly Detection using Gaussian Process Active Learning. Proceedings of the 15th IEEE International Conference on Information Fusion (FUSION), 1164–1171.Google Scholar
Lane, R.O., Nevell, D.A., Hayward, S.D. and Beaney, T. W. (2010). Maritime Anomaly Detection and Threat Assessment. Proceedings of the 13th IEEE Conference on Information Fusion (FUSION), 1–8.Google Scholar
Last, P., Bahlke, C., Hering-Bertram, M. and Linsen, L. (2014). Comprehensive Analysis of Automatic Identification System (AIS) Data in Regard to Vessel Movement Prediction. The Journal of Navigation, 67, 791–809. DOI: 10.1017/S0373463314000253.CrossRefGoogle Scholar
Laxhammar, R. and Falkman, G. (2011). Sequential Conformal Anomaly Detection in Trajectories based on Hausdorff Distance. Proceedings of the 14th IEEE International Conference on Information Fusion (FUSION), 1–8.Google Scholar
Laxhammar, R. and Falkman, G. (2012). Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor. In : Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., and Sioutas, S. (eds.), AIAI 2012, Part II. IFIP AICT, vol. 382, 192–202, Springer, Heidelberg.Google Scholar
Laxhammar, R. and Falkman, G. (2015). Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories. Annals of Mathematics and Artificial Intelligence, 74, 67–94, DOI: 10.1007/s10472-013-9381-7.CrossRefGoogle Scholar
Lei, P.-R. (2015). A framework for anomaly detection in maritime trajectory behavior. Knowledge and Information Systems, 47(1), 189–214.CrossRefGoogle Scholar
Liu, B., De Souza, E.N., Matwin, S. and Sydow, M. (2014). Knowledge-based clustering of ship trajectories using density-based approach. Proceedings of the IEEE International Conference on Big Data (Big Data), 603–608.CrossRefGoogle Scholar
Liu, B., De Souza, E.N., Hilliard, C. and Matwin, S. (2015). Ship movement anomaly detection using specialized distance measures. Proceedings of the 18th IEEE International Conference on Information Fusion (Fusion), 1113–1120.Google Scholar
Maggi, F.M., Mooij, A.J. and van der Aalst, W.M.P. (2013). Analyzing Vessel Behavior using Process Mining. In : Van de Laar, P., Tretmans, J. and Borth, M. (eds.), Situation Awareness with Systems of Systems. Springer International Publishing Inc., 133–148, DOI: 10.1007/978-1-4614-6230-9_9.CrossRefGoogle Scholar
Mascaro, S., Nicholso, A.E. and Korb, K.B. (2014). Anomaly detection in vessel tracks using Bayesian networks. International Journal of Approximate Reasoning, 55(1), 84–98.CrossRefGoogle Scholar
McAbee, A., Scrofani, J., Tummala, M., Garren, D. and McEachen, J. (2014). Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness. Proceedings of the 17th IEEE International Conference on Information Fusion (FUSION), 1–6.Google Scholar
Osekowska, E., Johnson, H. and Carlsson, B. (2014). Grid size optimization for potential field based maritime anomaly detection. Transportation Research Procedia, 3, 720–729.CrossRefGoogle Scholar
Osekowska, E., Johnson, H. and Carlsson, B. (2015). Learning Maritime Traffic Rules Using Potential Fields. In: Corman, F., Voß, S. and Negenborn, R.R. (eds.), ICCL 2015, LNCS. Springer International Publishing Inc., 298–312, DOI: 10.1007/978-3-319-24264-4_21.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013a). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.CrossRefGoogle Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013b). Traffic Knowledge Discovery from AIS Data. Proceedings of the 16th IEEE International Conference on Information Fusion, pp. 1996–2003.Google Scholar
Pallotta, G. and Jousselme, A.-L. (2015). Data-driven detection and context-based classification of maritime anomalies. Proceedings of the 18th IEEE International Conference on Information Fusion, 1152–1159.Google Scholar
Radon, A.N., Wang, K., Glässer, U., Wehn, H. and Westwell-Roper, A. (2015). Contextual verification for false alarm reduction in maritime anomaly detection. Proceedings of the IEEE International Conference on in Big Data (Big Data), 1123–1133.CrossRefGoogle Scholar
Roy, J. (2008). Anomaly Detection in the Maritime Domain. In Proceedings of SPIE 6945, Optics and Photonics in Global Homeland Security IV, 69450W, DOI: 10.1117/12.776230.Google Scholar
Shahir, H.Y., Glasser, U., Nalbandyan, N. and Wehn, H. (2014). Maritime Situation Analysis A Multi-vessel Interaction and Anomaly Detection Framework. Proceedings of the IEEE Joint Intelligence and Security Informatics Conference (JISIC), 192–199.Google Scholar
Shahir, H.Y., Glasser, U., Shahir, A.Y. and Wehn, H. (2015). Maritime Situation Analysis Framework: Vessel Interaction Classification and Anomaly Detection. Proceedings of the IEEE International Conference on Big Data (Big Data), 1279–1289.CrossRefGoogle Scholar
Silveira, P.A.M., Teixeira, A.P. and Guedes Soares, C. (2013). Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. The Journal of Navigation, 66, 879–898. DOI: 10.1017/S0373463313000519.CrossRefGoogle Scholar
Smith, M., Reece, S., Roberts, S. and Rezek, I. (2012). Online maritime abnormality detection using Gaussian process and extreme value theory. Proceedings of the 12th IEEE International Conference on Data Mining (ICDM), 645–654.Google Scholar
Smith, M., Reece, S., Roberts, S., Psorakis, I. and Rezek, I. (2014a). Maritime abnormality detection using Gaussian processes. Knowledge and Information Systems, 38, 717–741.CrossRefGoogle Scholar
Smith, J., Nouretdinov, I., Craddock, R., Offer, C., and Gammerman, A. (2014b). Anomaly detection of trajectories with kernel density estimation by conformal prediction. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds.), AIAI 2014 Workshops, IFIP AICT. IFIP International Federation for Information Processing, Inc., 271–280.Google Scholar
Smith, J., Nouretdinov, I., Craddock, R., Offer, C., and Gammerman, A. (2015). Conformal anomaly detection of trajectories with a multi-class hierarchy. In: Gammer man, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015, LNAI. Springer International Publishing Inc., Switzerland, 281–290, DOI: 10.1007/978-3-319-17091-6_23.Google Scholar
Soleimani, B.H., De Souza, E.N., Hilliard, C. and Matwin, S. (2015). Anomaly detection in maritime data based on geometrical analysis of trajectories. Proceedings of the IEEE 18th International Conference on Information Fusion (Fusion), 1100–1105.Google Scholar
Terroso-Saenz, F., Valdes-Vela, M. and Skarmeta-Gomez, A.F. (2015). A complex event processing approach to detect abnormal behaviours in the marine environment. Information Systems Frontiers, 18(79), 1–16.Google Scholar
Tijardovic, I. (2009). The Use of AIS for Collision Avoidance. The Journal of Navigation, 62(1), 168–172. DOI: 10.1017/S0373463308005055.CrossRefGoogle Scholar
Vandecasteele, A., Devillers, R. and Napoli, A. (2013). A Semi-Supervised Learning Framework Based on Spatio-Temporal Semantic Events for Maritime Anomaly Detection and Behavior Analysis. Proceedings of the 11th International Symposium for GIS and Computer Cartography for Coastal Zones Management.Google Scholar
Venskus, J., Kurmis, M., Andziulis, A. and Lukošius, Ž. (2015). Self-Learning Adaptive Algorithm for Maritime Traffic Abnormal Movement Detection based on Virtual Pheromone Method. Proceedings of the IEEE International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 1–6.Google Scholar
Vovk, V., Gammerman, A. and Shafer, G. (2005). Algorithmic learning in a random world. Springer.Google Scholar
Wang, X., Liu, X., Liu, B., De Souza, E.N. and Matwin, S. (2014). Vessel route anomaly detection with Hadoop mapreduce. Proceedings of the IEEE International Conference on Big Data (Big Data), 25–30.CrossRefGoogle Scholar
Wijaya, W.M. and Nakamura, Y. (2013). Predicting Ship Behavior Navigating Through Heavily Trafficked Fairways by Analyzing AIS Data on Apache HBase. Proceedings of the IEEE first International Symposium on Computing and Networking, 220–226.Google Scholar
Wu, Y., Patterson, A., Santos, R.D.C., and Vijaykumar, N.L. (2014). Topology preserving mapping for maritime anomaly detection. In : Murgante, B. et al. (Eds.): ICCSA 2014, Part VI, LNCS. Springer International Publishing Inc., 313–326, DOI: 10.1007/978-3-319-09153-2_24.Google Scholar