Abstract
The appearance of the smart houses, buildings, and cities has defined new attack scenarios targeting industrial information systems. The paper suggests a visualization-driven approach to the analysis of the data from heating, ventilating and conditioning system (HVAC). The key element of the approach is the RadViz visualization that is used to form daily operation patterns and can detect suspicious deviations that could be the signs of fraudulent activity in the system. It is supplemented by a matrix-based representation of the HVAC parameters that is constructed in the way that allows highlighting changes in values of parameters being analyzed. The distinctive feature of the proposed visualization models is the ability to display data from different data sources. To demonstrate and evaluate the efficiency of the proposed approach we used the VAST MiniChallenge-2 2016 data set that contains logs from the HVAC system and the access control system.
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Novikova, E., Bestuzhev, M., Kotenko, I. (2020). Anomaly Detection in the HVAC System Operation by a RadViz Based Visualization-Driven Approach. In: Katsikas, S., et al. Computer Security. CyberICPS SECPRE SPOSE ADIoT 2019 2019 2019 2019. Lecture Notes in Computer Science(), vol 11980. Springer, Cham. https://doi.org/10.1007/978-3-030-42048-2_26
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