Visual Analytics of Anomalous User Behaviors: A Survey
This is an incremental survey paper that organizes existing research for practitioners and researchers in anomaly detection.
This paper surveys visual analytics methods for detecting anomalous user behaviors across four application domains, examining data types, detection techniques, and visualization approaches while identifying research gaps and future directions.
The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.