Abnormal Ship Behavior Detection Based on AIS Data
Source abstract
Abstract
With the development of navigation globalization and ship dehumanization, the contradiction between the increasing demand for ship behavior supervision and limited traffic service resources is obvious, and the frequent occurrence of accidents at sea is a problem. The monitoring of abnormal ship behavior is an important link in maritime transportation. With the popularization of the automatic identification system and increasing research in the maritime field, the AIS is widely used in the management of ship static information and the real-time sharing of dynamic information. The generated moving ship trajectory data provide a new opportunity for research into abnormal ship behavior and its detection. In light of the current situation of abnormal ship behavior research, we detected abnormal ship behavior from the point of view of spatial information and thematic information based on moving ship trajectory data. Therefore, this study first modeled the cognition of abnormal ship behavior. Then, based on the cognition of group ship behavior rules, we used a method based on graph structure learning to mine maritime routes from the perspective of ship spatial information. Next, we used Rayda’s criterion to detect the anomalous behavior of ships in space. Then, based on the isolation forest algorithm, we detected and described the abnormal behavior shown by ship thematic information. The experimental results show that the framework proposed in this paper can effectively detect the abnormal behavior of ships.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Navigation and Safety · Maritime Security and History · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Funding
National Natural Science Foundation of China · Key Program of the National Natural Science Foundation of China · Central South University
References (33)
Show source-provided references
1. Ship trajectory uncertainty prediction based on a Gaussian Process model10.1016/j.oceaneng.2019.04.024 ↗
2. Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction10.3390/e15062218 ↗
3. Mou, N., Ren, H., Zheng, Y., Chen, J., Niu, J., Yang, T., Zhang, L., and Liu, F. (2021). Traffic Inequality and Relations in Maritime Silk Road: A Network Flow Analysis. ISPRS Int. J. Geo-Inf., 10.10.3390/ijgi10010040 ↗
4. He, J. (2017). ‘One Belt, One Road’: China's New Strategy and Its Impact on FDI. Asia's Changing International Investment Regime, Springer.10.1007/978-981-10-5882-0_10 ↗
5. Lee, E.S., Mokashi, A.J., Sang, Y.M., and Kim, G.S. (2019). The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation. J. Mar. Sci. Eng., 7.10.3390/jmse7090287 ↗
6. A framework for anomaly detection in maritime trajectory behavior10.1007/s10115-015-0845-4 ↗
7. How big data enriches maritime research—A critical review of Automatic Identification System (AIS) data applications10.1080/01441647.2019.1649315 ↗
8. Ristic, B., La Scala, B., Morelande, M., and Gordon, N. (July, January 30). Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction. Proceedings of the 2008 11th International Conference on Information Fusion, Cologne, Germany.
9. Zhaolin, W. (1993). Maritime Traffic Engineering, Maritime Traffic Engineering.
10. Holst, A., Bjurling, B., Ekman, J., Rudström, Å., Wallenius, K., Björkman, M., Fooladvandi, F., Laxhammar, R., and Trönninger, J. (2012, January 9–12). A Joint Statistical and Symbolic Anomaly Detection System: Increasing performance in maritime surveillance. Proceedings of the 2012 15th International Conference on Information Fusion, Singapore.
11. Soleimani, B.H., De Souza, E.N., Hilliard, C., and Matwin, S. (, January 6–July). Anomaly Detection in Maritime Data Based on Geometrical Analysis of Trajectories. Proceedings of the 18th International Conference on Information Fusion (Fusion), Washington, DC, USA.
12. Data mining approach to shipping route characterization and anomaly detection based on AIS data10.1016/j.oceaneng.2020.106936 ↗
13. Monitoring of abnormal movement patterns of ferry based on AIS data
14. Laxhammar, R. (July, January 30). Anomaly detection for sea surveillance. Proceedings of the 2008 11th International Conference on Information Fusion, Cologne, Germany.
15. Bomberger, N.A., Rhodes, B.J., Seibert, M., and Waxman, A.M. (2006, January 10). Associative learning of vessel motion patterns for maritime situation awareness. Proceedings of the 9th International Conference on Information Fusion, Florence, Italy.10.1109/ICIF.2006.301661 ↗
16. Smith, M., Reece, S., Roberts, S., and Rezek, I. (2012, January 10–13). Online Maritime Abnormality Detection using Gaussian Processes and Extreme Value Theory. Proceedings of the 12th IEEE International Conference on Data Mining (ICDM), Brussels, Belgium.10.1109/ICDM.2012.137 ↗
17. Venskus, J., Treigys, P., Bernatavičienė, J., Tamulevičius, G., and Medvedev, V. (2019). Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding. Sensors, 19.10.3390/s19173782 ↗
18. Shahir, H.Y., Glässer, U., Nalbandyan, N., and Wehn, H. (2014, January 24–26). Maritime Situation Analysis: A Multi-vessel Interaction and Anomaly Detection Framework. Proceedings of the 2014 IEEE Joint Intelligence and Security Informatics Conference, The Hague, The Netherlands.10.1109/JISIC.2014.36 ↗
19. Pallotta, G., and Jousselme, A.L. (2015, January 6–9). Data-driven Detection and Context-based Classification of Maritime Anomalies. Proceedings of the 18th International Conference on Information Fusion (Fusion), Washington, DC, USA.
20. Grid Size Optimization for Potential Field based Maritime Anomaly Detection10.1016/j.trpro.2014.10.051 ↗
21. Venskus, J., Kurmis, M., Andziulis, A., Lukosius, Z., Voznak, M., and Bykovas, D. (2015, January 27–29). Self-Learning Adaptive Algorithm for Maritime Traffic Abnormal Movement Detection based on Virtual Pheromone Method. Proceedings of the 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Chicago, IL, USA.10.1109/SPECTS.2015.7285281 ↗
22. Maritime anomaly detection: A review10.1002/widm.1266 ↗
23. Automated anomaly detection processor10.1117/12.474940 ↗
24. Riveiro, M., Falkman, G., and Ziemke, T. (2008, January 9–11). Visual analytics for the detection of anomalous maritime behavior. Proceedings of the 12th International Conference Information Visualisation 2008, London, UK.10.1109/IV.2008.25 ↗
25. Anomaly detection in vessel tracks using Bayesian networks10.1016/j.ijar.2013.03.012 ↗
26. Radon, A.N., Wang, K., Glässer, U., Wehn, H., and Westwell-Roper, A. (November, January 29). Contextual verification for false alarm reduction in maritime anomaly detection. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.10.1109/BigData.2015.7363866 ↗
27. Using Agents to Improve International Maritime Transport Security10.1109/MIS.2011.23 ↗
28. Laxhammar, R., Falkman, G., and Sviestins, E. (2009, January 6–9). Anomaly detection in sea traffic—A comparison of the Gaussian Mixture Model and the Kernel Density Estimator. Proceedings of the 12th International Conference on Information Fusion, Seattle, WA, USA.
29. Arguedas, V.F., Mazzarella, F., and Vespe, M. (2015, January 18–21). Spatio-temporal Data Mining for Maritime Situational Awareness. Proceedings of the Oceans 2015 Genova, Ctr Congressi, Genova, Genova, Italy.10.1109/OCEANS-Genova.2015.7271544 ↗
30. Jasinevicius, R., and Petrauskas, V. (2008, January 27–29). Fuzzy Expert Maps for Risk Management Systems. Proceedings of the IEEE/OES Us/EU-Baltic International Symposium, Tallinn, Estonia.10.1109/BALTIC.2008.4625491 ↗
31. Bautista-Sánchez, R., Barbosa-Santillan, L.I., and Sánchez-Escobar, J. (2021). Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation. Appl. Sci., 11.10.3390/app11052429 ↗
32. Automatic Generation of Road Maps from Low Quality GPS Trajectory Data via Structure Learning10.1109/ACCESS.2018.2882581 ↗
33. Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15–19). Isolation Forest. Proceedings of the Eighth IEEE International Conference on Data Mining, Pisa, Italy.10.1109/ICDM.2008.17 ↗