The Importance of Visualization and Interaction in the Anomaly Detection Process

Author:

Riveiro Maria1

Affiliation:

1. Informatics Research Centre, University of Skövde, Skövde, Sweden

Abstract

Large volumes of heterogeneous data from multiple sources need to be analyzed during the surveillance of large sea, air, and land areas. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems for area surveillance are rarely used in the real world since these capabilities and applications present two critical challenges: they need to provide adequate user support and they need to involve the user in the underlying detection process. Visualization and interaction play a crucial role in providing adequate user support and involving the user in the detection process. Therefore, this chapter elaborates on the role of visualization and interaction in the anomaly detection process, using the surveillance of sea areas as a case study. After providing a brief description of how operators identify conflict traffic situations and anomalies, the anomaly detection problem is characterized from a data mining point of view, suggesting how operators may enhance the process through visualization and interaction.

Publisher

IGI Global

Reference40 articles.

1. Axelsson, S. (2005). Understanding Intrusion Detection Through Visualization. (Ph.D. thesis). Goteborg, Sweden: Chalmers University of Technology.

2. Bomberger, N., Rhodes, B., Seibert, M., & Waxman, A. (2006). Associative learning of vessel motion patterns for maritime situation awareness. In Proceedings of 9th International Conference on Information Fusion. New Brunswick, NJ: IEEE Press.

3. Visual Mining of Association Rules

4. Cai, Y. & de M. Franco, R. (2009). Interactive visualization of network anomalous events. In: Computational Science, 5544, 450–459. Berlin: Springer.

5. Dahlbom, A., & Niklasson, L. (2007). Trajectory clustering for coastal surveillance. In Proceedings of the 10th International Conference on Information Fusion. QC, Canada: IEEE Press.

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