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Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier

Published online by Cambridge University Press:  16 January 2017

Rong Zhen ,
Yongxing Jin ,
Qinyou Hu ,
Zheping Shao  and
Nikitas Nikitakos
Rong Zhen*
Affiliation:
(Merchant Marine College, Shanghai Maritime University, China)
Yongxing Jin
Affiliation:
(Merchant Marine College, Shanghai Maritime University, China)
Qinyou Hu
Affiliation:
(Merchant Marine College, Shanghai Maritime University, China)
Zheping Shao
Affiliation:
(Navigation College, Ji Mei University, China)
Nikitas Nikitakos
Affiliation:
(Merchant Marine College, Shanghai Maritime University, China) (Department of Shipping Trade and Transport, University of the Aegean, Greece)
*
(E-mail: zrandsea@163.com)
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Abstract

Maritime anomaly detection is a key technique in intelligent vessel traffic surveillance systems and implementation of maritime situational awareness. In this paper, we propose a method which combines vessel trajectory clustering and Naïve Bayes classifier to detect anomalous vessel behaviour in the maritime surveillance system. A similarity measurement between vessel trajectories is designed based on the spatial and directional characteristics of Automatic Identification System (AIS) data, then the method of hierarchical and k-medoids clustering are applied to model and learn the typical vessel sailing pattern within harbour waters. The Naïve Bayes classifier of vessel behaviour is built to classify and detect anomalous vessel behaviour. The proposed method has been tested and validated on the vessel trajectories from AIS data within the waters of Xiamen Bay and Chengsanjiao, China. The results indicate that the proposed method is effective and helpful, thus enhancing maritime situational awareness in coastal waters.

Keywords

Maritime anomaly detectionVessel trajectories clusteringNaïve Bayes classifierAIS
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 3 , May 2017 , pp. 648 - 670
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Aarsæther, K.G. and Moan, T. (2009). Estimating Navigation Patterns from AIS. Journal of Navigation, 62, 587–607.CrossRefGoogle Scholar
Bishop, C.M., (2006). Pattern Recognition and Machine Learning. New York, USA: Springer Science Business Media.Google Scholar
Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1–27.Google Scholar
Dahlbom, A. and Niklasson, N. (2007). Trajectory Clustering for Coastal Surveillance. The 10th International Conference on Information Fusion, Quebec City, Canada.CrossRefGoogle Scholar
Hamed, Y.S., Uwe, G. and Amir, Y.S. (2015). Maritime Situation Analysis Framework Vessel Interaction Classification and Anomaly Detection. 3rd IEEE International Conference on Big Data, IEEE Big Data 2015,Santa Clara, United States.Google Scholar
Han, J.W. and Micheline, K. (2006). Data Mining Concepts and Techniques. Elsevier Inc, San Francisco, USA.Google Scholar
Heij, C., Bijwaard, G.E. and Knapp, S. (2011). Ship inspection strategies: effects on maritime safety and environmental protection. Transport Research Part D, 16, 42–48.CrossRefGoogle Scholar
Hu, H.Y., Wang, Q.N., Qu, Z.W. and Li, Z.H. (2011). Spatial pattern recognition and abnormal traffic behavior detection of moving object. Journal of Jilin University Engineering and Technology Edition, 41(5), 1598–1602 (in Chinese).Google Scholar
Jousselme, A. L. and Pallotta, G. (2015). Dissecting uncertainty-based fusion techniques for maritime anomaly detection. 18th International Conference on Information Fusion, Washington, DC USA.Google Scholar
Lane, R.O., Nevell, D.A., Hayward, S.D. and Beaney, T.W. (2010). Maritime anomaly detection and threat assessment. Proceedings of 13th Conference on Information Fusion, Edinburgh, UK.Google Scholar
Laxhammar, R. (2008). Anomaly Detection for Sea Surveillance. The 11th International Conference on Information Fusion, Cologne, Germany, 55–62.Google Scholar
Lei, P.R. (2015). A framework for anomaly detection in maritime trajectory behaviour. Knowledge and Information. 44, 1–26.Google Scholar
Liu, C.Q. and Chen, X.Q. (2013). Inference of Single Vessel Behavior with Incomplete Satellite-based AIS Data. Journal of Navigation, 66, 813–823.CrossRefGoogle Scholar
Marucci-Wellman, H.R, Lehtob, M.R. and Corns, H.L. (2015). A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naïve Bayes algorithms. Accident Analysis and Prevention, 84, 165–176.CrossRefGoogle ScholarPubMed
Morris, B.T and Trivedi, M.M. (2008). A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance. IEEE Transactions on Circuits and Systems for Video Technology, 18(7), 1114–1127.Google Scholar
Mou, J.M., Van der Tak, C. and Ligteringen, H. (2010). Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering, 37, 483–490.Google Scholar
Mukherjee, S. and Sharma, N. (2012). Intrusion detection using Naïve Bayes classifier with feature reduction. Procedia Technology, 4, 119–128.Google Scholar
Noaman, H.M., Elmougy, S., Ghoneim, A. and Hamza, T. (2010). Nayes Bayes Classifier based Arabic document categorization. The 7th International Conference on Informatics and Systems, Dokki, Giza, Egypt.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2012). Traffic Knowledge Discovery from AIS Data. 15th International Conference on Information Fusion, Singapore.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel Pattern Knowledge Discovery From AIS Data: a Framework for Anomaly Detection and Route Prediction. Entropy, 15, 2288–2315.CrossRefGoogle Scholar
Qu, L., Zhou, F. and Chen, Y.W. (2009). Trajectory classification based on Hausdorff distance for visual surveillance system. Journal of Jilin University Engineering and Technology Edition, 39(5), 1618–1692. (In Chinese)Google Scholar
Rhodes, B.J., Bomberger, N.A., Seibert, M. and Waxman, A.M. (2005). Maritime situation monitoring and situation awareness using learning mechanisms. Military Communications Conference, Atlantic City, NY, USA.Google Scholar
Rhodes, B.J., Bomberger, N.A. and Zandipour, M. (2007). Probabilistic Associative Learning of Vessel Motion Patterns at Multiple Spatial Scales for Maritime Situation Awareness. The 10th International Conference on Information Fusion. Québec City, Canada.CrossRefGoogle Scholar
Riveiro, M. (2014). Evaluation of Normal Model Visualization for Anomaly Detection in Maritime Traffic. ACM Transactions on Interactive Intelligent Systems, 5:1–5:24.CrossRefGoogle Scholar
Riveiro, M., Johansson, F., Falkman, G., and Ziemke, T. (2008). Supporting maritime situation awareness using self-organizing maps and Gaussian mixture models. Frontiers in Artificial Intelligence and Applications, 173, 84.Google Scholar
Shao, Z.P., Sun, T.D., Pan, J.C. and Ji, X.B. (2007). Vessel information service system based on ECDIS and AIS. The 5th International Conference on Transportation Engineering Processing, Chengdu, China.Google Scholar
Silveira, P.A.M, Teixeira, A.P. and Guedes Soares, C. (2013). Use of AIS Data to Characterize Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. Journal of Navigation, 66, 879–898.Google Scholar
Tun, M. H., Chambers, G. S., Tan, T. and Ly, T. (2007). Maritime Port Intelligence Using AIS Data. RNSA Security Technology Conference, Melbourne, Australia, 33–43.Google Scholar
Varuna, S. and Natesan, P. (2015). An Integration of K-Means Clustering and Naïve Bayes Classifier for Intrusion Detection. 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), Chennai, India.Google Scholar
Weng, J.X. and Xue, S. (2015). Ship Collision Frequency Estimation in Port Fairways: A Case Study, Journal of Navigation, 68, 602–618.Google Scholar
Yuan, G., Xia, S.X., Zhang, L., Zhou, Y. (2011). Trajectory clustering algorithm based on structural similarity. Journal of Communications, 32 (9), 103–110 (in Chinese).Google Scholar
Zhang, W.B., Goerlandt, F., Kujala, P. and Wang, Y.H. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124, 141–156.Google Scholar
Zhen, R., Shao, Z.P., Pan, J.C. and Zhao, Q. (2015). Lateral Distribution Regularity of Ship' Sailing Position within the Channel Based on the AIS Data. The 5th International Conference on Transportation Engineering Processing, Dalian, China.Google Scholar