Abstract
Detection of anomalies in employees’ movement represents an area of considerable interest for cyber-physical security applications. In the paper the visual analytics approach to detection of the spatiotemporal patterns and anomalies in organization stuff movement is proposed. The key elements of the approach are interactive self-organizing maps used to detect groups of employees with similar behavior and heat map applied to detect anomalies. They are supported by a set of the interactive interconnected visual models aimed to present spatial and temporal route patterns. We demonstrate our approach with an application to the VAST MiniChallenge-2 2016 data set, which describes movement of the employees within organization building.
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References
Millonig, A., Maierbrugger, M.: Identifying unusual pedestrian movement behavior in public transport infrastructures. In: Proceedings of Movement Pattern Analysis Workshop (MPA2010), pp. 106–110. Zurich (2010)
Lerman, Y., Rofe, Y., Omer, I.: Using space syntax to model pedestrian movement in urban transportation planning. Geogr. Anal. 46(4), 392–410 (2014)
Pan, X., Han, C., Dauber, K., Law, K.: A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations. AI Soc. 22, 113–132 (2007)
Vast Challenge Homepage. http://vacommunity.org/. Accessed 10 Mar 2017
Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering: a survey. In: Data Mining and Knowledge Discovery Handbook, pp. 855–874 (2010)
Andrienko, N., Andrienko, G.: Visual analytics of movement: an overview of methods, tools and procedures. Inf. Vis. 12(1), 3–24 (2013)
Novikova, E., Kotenko, I.: Analytical visualization techniques for security information and event management. In: Proceedings of 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 519–525 (2013)
Schreck, T., Bernard, J., Von Landesberger, T., Kohlhammer, J.: Visual cluster analysis of trajectory data with interactive Kohonen maps. Inf. Vis. 8(1), 14–29 (2009)
Andrienko, G., Andrienko, N.: Exploration of massive movement data: a visual analytics approach. In: Proceedings of 11th AGILE International Conference on Geographic Information Science (2008)
Guo, H., et al.: Tripvista: triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection. In: Proceedings of IEEE Pacific Visualization Symposium (PacificVis), pp. 163–170 (2011)
Sander, N., Abel, J., Bauer, R., Schmidt, J.: Visualising migration flow data with circular plots. In: European Population Conference (2014)
Andrienko, G., Andrienko, N., Schumann, H., Tominski, C.: Visualization of trajectory attributes in space–time cube and trajectory wall. In: Buchroithner, M., Prechtel, N., Burghardt, D. (eds.) Lecture Notes in Geoinformation and Cartography. Cartography from Pole to Pole, pp. 157–163. Springer, Heidelberg (2014)
Guo, C., et al.: Dodeca-rings map: interactively finding patterns and events in large geo-temporal data. In: IEEE Symposium on Visual Analytics Science and Technology, pp. 353–354 (2014)
Kohonen, T., Honkela, T.: Kohonen network. http://www.scholarpedia.org/article/Kohonen_network. Accessed 10 Mar 2017
Ultsch, A.: Self-organizing neural networks for visualization and classification. Information and Classification, pp. 307–313 (1993)
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This research has been supported by grant of the RFBR # 16-07-00625.
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Novikova, E., Murenin, I. (2017). Visualization-Driven Approach to Anomaly Detection in the Movement of Critical Infrastructure. In: Rak, J., Bay, J., Kotenko, I., Popyack, L., Skormin, V., Szczypiorski, K. (eds) Computer Network Security. MMM-ACNS 2017. Lecture Notes in Computer Science(), vol 10446. Springer, Cham. https://doi.org/10.1007/978-3-319-65127-9_5
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DOI: https://doi.org/10.1007/978-3-319-65127-9_5
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