Abstract
In many sensor systems used in urban environments, the amount of data produced can be vast. To aid operators of such systems, high-level information fusion can be used for automatically analyzing the surveillance information. In this paper an anomaly detection approach for finding areas with traffic patterns that deviate from what is considered normal is evaluated. The use of such approaches could help operators in identifying areas with an increased risk for ambushes or improvised explosive devices (IEDs).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ayling, S., Benchoam, D.: New antennas for new battlefields. In: Proceedings of the Military Communications and Information Systems Conference, Canberra, Australia, November 9-11 (2010)
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 11th International Conference on Information Fusion, Cologne, Germany, June 30 - July 3 (2008)
Kraiman, J.B., Arouh, S.L., Webb, M.L.: Automated anomaly detection processor. SPIE, vol. 4716, pp. 128–137 (2002)
Brax, C., Niklasson, L., Smedberg, M.: Finding behavioural anomalies in public areas using video surveillance data. In: Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, June 30 - July 3, pp. 1655–1662 (2008)
Laxhammar, R., Falkman, G., Sviestins, E.: Anomaly detection in sea traffic - a comparison of the gaussian mixture model and the kernel density estimator. In: Information Fusion, pp. 756–763 (2009)
Bomberger, N., Rhodes, B., Seibert, M., Waxman, A.: Associative learning of vessel motion patterns for maritime situation awareness. In: 2006 9th International Conference on Information Fusion, pp. 1–8 (2006)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3) (2009)
Patcha, A., Park, J.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks 51(12), 3448–3470 (2007)
Tan, P.N., Steinbach, M., Vipin, K.: Introduction to Data Mining. Pearson Education, Inc., Boston (2006)
Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA 2001), pp. 5–8 (2001)
Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Processing 83(12), 2481–2497 (2003)
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers and Security 28(1-2), 18–28 (2009)
Riveiro, M., Falkman, G., Ziemke, T.: Improving maritime anomaly detection and situation awareness through interactive visualization. In: Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, June 30 - July 3, pp. 47–54 (2008)
Johansson, F., Falkman, G.: Detection of vessel anomalies - a bayesian network approach. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 395–400 (2007)
Mascaro, S., Korb, K.B., Nicholson, A.E.: Learning abnormal vessel behaviour from ais data with bayesian networks at two time scales. Technical report, TR 2010 / Bayesian Intelligence (2010)
Laxhammar, R., Falkman, G.: Conformal prediction for distribution-independent anomaly detection in streaming vessel data. In: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques (StreamKDD 2010), Washington D.C., USA, July 25, pp. 47–55 (2010)
Dahlbom, A., Niklasson, L.: Trajectory clustering for coastal surveillance. In: Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Québec, Canada, July 9-12 (2007)
Snidaro, L., Piciarelli, C., Foresti, G.L.: Fusion of trajectory clusters for situation assessment. In: Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July 10-13 (2006)
Saab AB: Intelligent behaviour detector. Internet, http://www.saabgroup.com/Global/Documents%20and%20Images/Civil%20Security/Maritime%20Transportation%20and%20Port%20Security/IBD/ibd-ver1-090505a.pdf (accessed January 28, 2012)
Saab AB: Sirs radar sensor. Internet, http://www.saabgroup.com/Global/Documents%20and%20Images/Civil%20Security/Land%20Transport%20and%20Urban%20Security/SIRS%20200ITS/SIRS200ITS_downloads_Product%20sheet.pdf (accessed December 28, 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brax, C., Dahlbom, A. (2012). A Study of Anomaly Detection in Data from Urban Sensor Networks. In: Torra, V., Narukawa, Y., López, B., Villaret, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2012. Lecture Notes in Computer Science(), vol 7647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34620-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-34620-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34619-4
Online ISBN: 978-3-642-34620-0
eBook Packages: Computer ScienceComputer Science (R0)