Multiple queries with conditional attributes (QCATs) for anomaly detection and visualization
Simon Walton, Eamonn Maguire, Min Chen · 2014
This paper describes a visual analytics method for visualizing the effects of multiple anomaly detection models, exploring the complex model space of a specific type of detection method, namely Query with Conditional Attributes (QCAT), and facilitating the construction of composite models using multiple QCATs. We have developed a prototype system that features a browser-based interface, and database-driven back end. We tested the system using the "Inside Threats Dataset" provided by CMU.