The Visual Analytics Approach for Analyzing Trajectories of Critical Infrastructure Employers

Evgenia Novikova, Igor Kotenko and Ivan Murenin
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Evgenia Novikova: Department of Information Systems, Saint Petersburg State Electrotechnical University, 197022 Saint Petersburg, Russia
Igor Kotenko: Laboratory of Computer Security Problems, Saint Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 199178 Saint Petersburg, Russia
Ivan Murenin: Laboratory of Computer Security Problems, Saint Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 199178 Saint Petersburg, Russia

Energies, 2020, vol. 13, issue 15, 1-30

Abstract: Employees of different critical infrastructures, including energy systems, are considered to be a security resource, and understanding their behavior patterns may leverage user and entity behavior analytics and improve organization capabilities in information threat detection such as insider threat and targeted attacks. Such behavior patterns are particularly critical for power stations and other energy companies. The paper presents a visual analytics approach to the exploratory analysis of the employees’ routes extracted from the logs of the access control system. Key elements of the approach are interactive self-organizing Kohonen maps used to detect groups of employees with similar movement trajectories, and heat maps highlighting possible anomalies in their movement. The spatiotemporal patterns of the routes are presented using a Gantt chart-based visualization model named BandView. The paper also discusses the results of efficiency assessment of the proposed analysis and visualization models. The assessment procedure was implemented using artificially generated and real-world data. It is demonstrated that the suggested approach may significantly increase the efficiency of the exploratory analysis especially under the condition when no prior information on existing employees’ moving routine is available.

Keywords: visual analytics; data mining; moving entities; route patterns; anomaly detection; self-organizing Kohonen maps; Gantt chart-based visualization; heat maps (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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