Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection
    Download PDF
Ian Turk,Matthew Sinda,Xin'an Zhou,Jun Tao,Chaoli Wang,Qi Liao. Exploration of Linked Anomalies in Sensor Data for Suspicious Behavior Detection. International Journal of Software and Informatics, 2016,10(3):0
Hits: 452
Download times: 164
Abstract:We present a visual analytics system to understand the operation data of a company, GAStech, from IEEE VAST Challenge 2016. The data include proximity data recording the locations and movements of employees, and heating, ventilation, and air conditioning (HVAC) data recording the environmental conditions in the building. Analyzing the data to detect the suspicious behaviors of some disgruntled employees is of special interest. Our system provides coordinated multiple views to visualize the proximity data and the HVAC data over time. Visual hints and comparisons are designed for users to identify abnormal patterns and compare them. Furthermore, the system automatically detects and correlates the anomalies in the data. We provide use cases to demonstrate the effectiveness of our system.
keywords:visual analytics  sensor data  security
View Full Text  View/Add Comment  Download reader

 

 

more>>  
Visitor:1715639
Top Paper  |  FAQ  |  Guest Editors  |  Email Alert  |  Links  |  Copyright  |  Contact Us

© Copyright by Institute of Software, the Chinese Academy of Sciences
ICP: Jing ICP Bei No.10016592

京公网安备 11040202500065号