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Volume: 33 | Article ID: art00005
A Visual Analytics Approach for Anomaly Detection from a Novel Traffic Light Data
  DOI :  10.2352/ISSN.2470-1173.2021.1.VDA-330  Published OnlineJanuary 2021

Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. Data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns.

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Glenn Turner, Guoning Chen, Yunpeng Zhang, "A Visual Analytics Approach for Anomaly Detection from a Novel Traffic Light Datain Proc. IS&T Int’l. Symp. on Electronic Imaging: Visualization and Data Analysis,  2021,  pp 330-1 - 330-13,

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