MARITIME ANOMALY DETECTION USING AIS DATA STREAMS
DOI:
https://doi.org/10.61841/3ag7gp04Keywords:
AIS, unlawful illicit conducT, trajectory reconstruction and recreation, abnormality location, vessel type detection,, variational random recurrent neural networks.Abstract
Marine vessels which are involved in some commercial activities follow certain patterns or paths depending on type of their commercial or business in which they are used. If vessels tend to follow some anomalous behavior, this might indicate that they are involved in some illicit behavior. We proposed a framework for detection of anomalous activities in marine using data streams obtained from Automatic Identification System (AIS). We use recurrent neural network with latent variable modeling and this combined with the AIS messages which embedded. This will create a new representation which will address major issues that will arise when AIS data streams are analyzed and considered as hug set of streaming information, which are unusual time sample and crowded data. This system will significantly concern on trajectory reconstruction, peculiarity and anomaly recognition, identification and vessel type detection.
Downloads
References
1. N. Perobelli, “MarineTraffic-A day in numbers,” Jun. 2016. [Online]. Available: https://www.marinetraffic.com/blog/a-day-in-numbers/
2. Luis Patino, James Ferryman, "Loitering Behaviour Detection of Boats at Sea", Computer Vision and Pattern Recognition Workshops (CVPRW) 2017 IEEE Conference on, pp. 2169-2175, 2017.
3. Guizhen Wang, Abish Malik, Calvin Yau, Chittayong Surakitbanharn, David S. Ebert, "TraSeer: A visual analytics tool for vessel movements in the coastal areas", Technologies for Homeland Security (HST) 2017 IEEE International Symposium on, pp. 1-6, 2017
4. Klimis Ntalianis, Emmanuel Sardis, Nicolas Tsapatsoulis, Anastasios Doulamis, Panagiotis Rizomiliotis, "Multiocular surveilance of wide dynamic environments based on optical vision event modelling and end-to-end data encryption: A cloud-based monitoring approach of maritime activities", Globecom Workshops (GC Wkshps) 2012 IEEE, pp. 742-746, 2012.
5. J. Will, L. Peel, and C. Claxton, “Fast Maritime Anomaly Detection using Kd-Tree Gaussian
Processes,” in IMA Maths in Defence Conference, 2011.
6. “Architecture of maritime awareness system supplied with external information.” Annual Pre-peer reviewed version. Riveiro M., Pallotta G., and Vespe . WIREs Data Mining Knowledge Discovery Advanced Review. 2018.
7. Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, Rene Garello and Ronan Fablet, “A Multi- task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics
8. Holst, P. Ryman, and A. Linse, “Stattistical Anomaly Detection for Maritime Surveillance and Monitoring,” in Maritime Knowledge Discovery and Anomaly Detection Workshop, Joint Research Centre, ISPRA, Italy, Jul. 2016.
9. Richard O Lance, Steve Hayward, David Nevell ,”Maritime Anomoly Detection and Threat Assessment”,2010 IEEE Conference Paper.
10. Mark Smith, Steven Reece, Stephen Roberts, Lead Rezek,”Online Maritime Abnormality Detection using Gaussian Processes and Extreme Value Theory”.
11. Maria Riverio, Giuliana Pallotia, Michele Vespe, “Maritime anomaly detection:A review”, Article in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery: May 2018
12. J. M. Beaver, R. A. Kerekes, J. N. Treadwell, An information fusion framework for threat assessment, 12th International Conference on Information Fusion, Seattle, WA, USA, 6-9 July 2009.
13. Zissis, D., Xidias, E., and Lekkas, D. (2015). “A cloud based architecture capable of perceiving and predicting multiple vessel behaviour, system.” Applied Soft Computing Journal.
14. J. Hartikainen and S. S¨arkk¨a, “Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process Regression Models,” in Proc of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2010.
15. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in Proceedings of the International Conference on Learning Representations (ICLR), 2015.
16. J. Barker, R. Green, P. Thomas, G. Brown, D. Salmond, A Bayesian information fusion decision support tool for the identification of difficult targets, Mathematics in Defence 2009, Farnborough, Hampshire, UK, 19 November 2009.
17. Zhou, J., Li, Z., Wang, Y., and Chen, F. (2013). “Transparent machine learning— revealing internal states of machine learning.” Proc. of IUI2013 Workshop on Interactive Machine Learning.
18. J. George, J. L. Crassidis, T. Singh, and A. M. Fosbury, “Anomaly Detection using Content-Aided Target Tracking,” Journal of Advances in Information Fusion, 2011.
19. M. Ammar and S. Le Hegarat-Mascle, “An A-Contrario Approach for Object Detection in Video Sequence,” International Journal of Pure and Apllied Mathematics, vol. 89, no. 2, Dec. 2013.
20. M. Markou and S. Singh, “Novelty Detection: A Review Part 1: Statistical Approaches,” Signal Processing, vol. 83, pp. 2481–2497, 2003.
21. Willems, N., van Hage, W. R., de Vries, G., Janssens, J. H., and Malais, V. (2010). “An integrated approach for visual analysis of a multisource moving objects knowledge base.” International Journal of Geographical Information Science.
22. J.-M. Bernard, An introduction to the imprecise Dirichlet model for multinomial data, International Journal of Approximate Reasoning, 2005.
23. Y. Bengio, A. Courville, and P. Vincent, “Representation Learning: A Review and New Perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence.
24. N. Ye, A Markov chain model of temporal behaviour for anomaly detection, IEEE Workshop on Information Assurance and Security, US Military Academy, West Point, NY, 6-7 June 2000.
25. Willems, N., Van De Wetering, H., and Van Wijk, J. J. (2009). “Visualization of vessel movements.” Computer Graphics Forum.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
