Abstract
This paper presents a MAS dedicated to abnormal behaviors detection and alerts triggering in the maritime surveillance area. This MAS uses anomalies issued from an experienced Rule Engine implementing maritime regulation. It evaluates ships behavior cumulating the importance of related anomalies and triggers relevant alerts towards human operators involved in maritime surveillance. These human operators evaluate triggered alerts and confirm or invalidate them. Invalidated alerts are sent back to the MAS for a learning step since it self-adapts anomalies values to be consistent with human operators feedbacks. This MAS is implemented in the context of the project I2C, an EU funded project dedicated to abnormal ships behavior detection and early identification of threats such as oil slick, illegal fishing, or lucrative criminal activities (e.g. goods, drugs, or weapons smuggling).
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References
Henocque, Y., Lafon, X.: EUs Strategy on Maritime & Environmental Issues in the Four Seas: multilateral approaches in the Baltic, Black, Caspian & Mediterranean Seas. The EU and Europes Sea Basins, Tallinn (2011)
Ince, A., Topuz, E., Panayirci, E.: Principles of Integrated Maritime Surveillance System. Springler, Netherlands (1999)
Rasheed, Z., Shafique, K., Yu, L., Lee, M., Ramnath, K., Choe, T.E., Javed, O., Haering, N.: Distributed Sensor Networks for Visual Surveillance. In: Distributed Video Sensor Networks, pp. 439–449 (2011)
Battistello, G., Ulmke, M., Koch, W.: Knowledge-aided multisensor data fusion for maritime surveillance. In: Proceedings of SPIE, vol. 8047 (2011)
Wang, B., Ye, M., Li, X., Zhao, F., Ding, J.: Abnormal crowd behavior detection using high-frequency and spatio-temporal features. In: Machine Vision and Applications, pp. 1–11 (2011)
Aliakbarpour, H., Khoshhal, K., Quintas, J., Mekhnacha, K., Ros, J., Andersson, M., Dias, J.: HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network. In: Camarinha-Matos, L.M. (ed.) Technological Innovation for Sustainability. IFIP AICT, vol. 349, pp. 277–285. Springer, Heidelberg (2011)
Morel, M., Claisse, S.: Integrated System for Interoperable sensors & Information sources for Common abnormal vessel behaviour detection & Collaborative identification of threat (I2C). In: Proc. of the Ocean and Coastal Observation: Sensors and Observing Systems, Numerical Models and Information Systems, Brest, France (2010)
Li, X., Xue, Y., Chen, Y., Mali, B.: Context-aware anomaly detection for electronic medical record systems. In: Proceedings of the 2nd USENIX Conference on Health Security and Privacy, p. 8 (2011)
Gupta, S., Hossain, L.: Towards near-real-time detection of insider trading behaviour through social networks. Computer Fraud & Security 2011(1), 7–16 (2011)
Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J.: Network Intrustion Detection. In: Optimization Based Data Mining: Theory and Applications, pp. 237–241 (2001)
Fok, V., Lingard, D.M.: Using a Genetic Algorithm to Optimise Maritime Surveillance Performed by Space-based Sensors. National Committee for Space Science & National Space Society of Australia, Barry Drive, Australian National University, Canberra, ACT, Australia (2011)
Celik, M., Dadaser-Celik, F., Dokuz, A.S.: Anomaly detection in temperature data using DBSCAN algorithm. In: IEEE Innovations in Intelligent Systems and Applications (INISTA), pp. 91–95 (2011)
Tan, K.: A multi-agent system for tracking the intent of surface contacts in ports and waterways. Naval Postgraduate School Monterey CA (2005)
Jakob, M., Vaněk, O., Urban, Š., Benda, P., Pěchouček, M.: AgentC: agent-based testbed for adversarial modeling and reasoning in the maritime domain. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 1641–1642 (2010)
Gupta, K.M., Aha, D.W., Moore, P.: Case-Based Collective Inference for Maritime Object Classification. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS, vol. 5650, pp. 434–449. Springer, Heidelberg (2009)
Nilsson, M., van Laere, J., Ziemke, T., Edlund, J.: Extracting rules from expert operators to support situation awareness in maritime surveillance. In: Proceedings of the Eleventh International Conference on Information Fusion. IEEE (2008)
Auslander, B., Gupta, K.M., Aha, D.W.: A comparative evaluation of anomaly detection algorithms for maritime video surveillance. In: Society of Photo-Optical Instrumentation Engineers (SPIE), vol. 8019, p. 3 (2011)
Bernon, C., Gleizes, M.-P., Peyruqueou, S., Picard, G.: ADELFE: A Methodology for Adaptive Multi-agent Systems Engineering. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS (LNAI), vol. 2577, pp. 156–169. Springer, Heidelberg (2003)
Gleizes, M.-P., Camps, V., Georgé, J.-P., Capera, D.: Engineering Systems Which Generate Emergent Functionalities. In: Weyns, D., Brueckner, S.A., Demazeau, Y. (eds.) EEMMAS 2007. LNCS (LNAI), vol. 5049, pp. 58–75. Springer, Heidelberg (2008)
Di Marzo Serugendo, G., Gleizes, M.-P., Karageorgos, A.: Self-organising Software - From Natural to Artificial Adaptation. Springer Natural Computing Series (2011)
Ristic, B., La Scala, B., Morelande, M., Gordon, N.: Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction. In: 11th International Conference on Information Fusion, pp. 1–7 (2008)
Mano, J.-P., Georgé, J.-P., Gleizes, M.-P.: Adaptive Multi-agent System for Multi-sensor Maritime Surveillance. In: Demazeau, Y., Dignum, F., Corchado, J.M., Pérez, J.B. (eds.) Advances in PAAMS. AISC, vol. 70, pp. 285–290. Springer, Heidelberg (2010)
DCNS: Description of Work. Project I2C (2010)
Tsankova, D.D., Georgieva, V.S.: From local actions to global tasks: simulation of stigmergy based foraging behavior. Intelligent Systems, 353–358 (2004)
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Brax, N., Andonoff, E., Gleizes, MP. (2012). A Self-adaptive Multi-Agent System for Abnormal Behavior Detection in Maritime Surveillance. In: Jezic, G., Kusek, M., Nguyen, NT., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems. Technologies and Applications. KES-AMSTA 2012. Lecture Notes in Computer Science(), vol 7327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30947-2_21
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DOI: https://doi.org/10.1007/978-3-642-30947-2_21
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