Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-27T18:22:12.905Z Has data issue: false hasContentIssue false
  • English
  • Français

Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network

Published online by Cambridge University Press:  08 February 2019

Liangbin Zhao  and
Guoyou Shi
Liangbin Zhao*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Guoyou Shi
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Maritime anomaly detection can improve the situational awareness of vessel traffic supervisors and reduce maritime accidents. In order to better detect anomalous behaviour of a vessel in real time, a method that consists of a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a recurrent neural network is presented. In the method presented, the parameters of the DBSCAN algorithm were determined through statistical analysis, and the results of clustering were taken as the traffic patterns to train a recurrent neural network composed of Long Short-Term Memory (LSTM) units. The neural network was applied as a vessel trajectory predictor to conduct real-time maritime anomaly detection. Based on data from the Chinese Zhoushan Islands, experiments verified the applicability of the proposed method. The results show that the proposed method can detect anomalous behaviours of a vessel regarding speed, course and route quickly.

Keywords

Vessel TrajectoriesAnomaly detectionDBSCANRecurrent neural network
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 4 , July 2019 , pp. 894 - 916
Copyright
Copyright © The Royal Institute of Navigation 2019 

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

Ankerst, M., Breunig, M.M., Kriegel, H.P. and Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. The 1999 ACM SIGMOD International Conference on Management of Data, Philadelphia, USA, 49–60.10.1145/304182.304187Google Scholar
Bomberger, N.A., Rhodes, B.J., Seibert, M. and Waxman, A.M. Associative learning of vessel motion patterns for maritime situation awareness. (2006). The 2006 9th International Conference on Information Fusion, Florence, Italy, 1–8.10.1109/ICIF.2006.301661Google Scholar
Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B. (1992). Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3(5), 698–713.10.1109/72.159059Google Scholar
Daranda, A. (2016). Neural Network Approach to Predict Marine Traffic. Transactions in Baltic Journal of Modern Computing, 4(3), 483.Google 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. In Proceedings of the KDD, Portland, Oregon, 226–231.Google Scholar
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.10.1162/neco.1997.9.8.1735Google Scholar
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Google Scholar
Laxhammar, R. (2008). Anomaly detection for sea surveillance. The 11th International Conference on Information Fusion, Cologne, Germany, 55–62.Google Scholar
Li, H., Liu, J., Liu, R. W., Xiong, N., Wu, K. and Kim, T. H. (2017). A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis. Sensors, 17(8), 1792.10.3390/s17081792Google Scholar
Liu, B., de Souza, E. N., Matwin, S. and Sydow, M. (2014). Knowledge-based clustering of ship trajectories using density-based approach. IEEE International Conference on Big Data, Washington, USA, 603–608.10.1109/BigData.2014.7004281Google Scholar
Lipton, Z. C., Berkowitz, J. and Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.Google Scholar
Nair, V. and Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. The 27th international conference on machine learning, Haifa, Israel, 807–814.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(6), 2218–2245.10.3390/e15062218Google Scholar
Pan, J., Jiang, Q. and Shao, Z. (2014). Trajectory clustering by sampling and density. Marine Technology Society Journal, 48(6), 74–85.10.4031/MTSJ.48.6.8Google Scholar
Perera, L. P. and Soares, C. G. (2010). Ocean vessel trajectory estimation and prediction based on extended kalman filter. The Second International Conference on Adaptive and Self-Adaptive Systems and Applications, Lisbon, Portugal, 14–20.Google Scholar
Rhodes, B.J., Bomberger, N.A., Seibert, M. and Waxman, A.M. (2005). Maritime situation monitoring and awareness using learning mechanisms. The 2005 Military Communications Conference, Atlantic City, USA, 646–652.10.1109/MILCOM.2005.1605756Google 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, Quebec, Canada, 1–8.10.1109/ICIF.2007.4408127Google Scholar
Ristic, B., La Scala, B., Morelande, M. and Gordon, N. (2008). Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction. The 11th International Conference on Information Fusion, Cologne, Germany, 1–7.Google Scholar
Riveiro, M., Pallotta, G. and Vespe, M. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1266.Google Scholar
Riveiro, M. (2011). Visual analytics for maritime anomaly detection, Doctoral dissertation, Örebro universitet.Google Scholar
Sidibé, A. and Shu, G. (2017). Study of automatic anomalous behaviour detection techniques for maritime vessels. The Journal of Navigation, 70(4), 847–858.10.1017/S0373463317000066Google Scholar
Stateczny, A. and Kazimierski, W. (2011). Multisensor tracking of marine targets - decentralized fusion of kalman and neural filters. International Journal of Electronics & Telecommunications, 57(1), 65–70.10.2478/v10177-011-0009-8Google Scholar
Wiersma, J. W. F. (2010). Assessing vessel traffic service operator situation awareness. Doctoral dissertation, Delft University of Technology.Google Scholar
Xu, T., Liu, X. and Yang, X. (2011). Ship Trajectory online prediction based on BP neural network algorithm. IEEE International Conference on Information Technology, Computer Engineering and Management Sciences, Nanjing, China, 103–106.Google Scholar
Yan, W., Wen, R., Zhang, A. N. and Yang, D. (2016). Vessel movement analysis and pattern discovery using density-based clustering approach. IEEE International Conference on Big Data, Washington, USA, 3798–3806.10.1109/BigData.2016.7841051Google Scholar
Zhao, L., Shi, G. and Yang, J. (2018). Ship Trajectories Pre-processing Based on AIS Data. The Journal of Navigation, 71(5), 1210–1230. doi:10.1017/S0373463318000188Google Scholar
Zhen, R., Jin, Y., Hu, Q., Shao, Z. and Nikitas, N. (2017a). Maritime anomaly detection within coastal waters based on vessel trajectory clustering and näive Bayes classifier. The Journal of Navigation, 70(3), 648–670.10.1017/S0373463316000850Google Scholar
Zhen, R., Riveiro, M. and Jin, Y. (2017b). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492–501.10.1016/j.oceaneng.2017.09.015Google Scholar