Visual Analytics of Anomalous User Behaviors: A Survey

Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao

Research output: Contribution to journalArticle

Abstract

With the pervasive use of information technologies, the increasing availability of data provides new opportunities for understanding user behaviors. Unearthing anomalies in user behavior is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. In this work, we survey state-of-the-art research work in visual analytics of anomalous user behaviors and classify them into four application domains, which are social interaction, travel, network communication, and financial transaction. We further examine the research work in each category in terms of data types, visualization techniques, and interactive analysis methods. We hope that our survey can provide systematic guidelines for researchers and practitioners to find effective solutions to their research problems in specific application domains. Finally, we discuss trends of academic interest over the past decades and suggest potential directions across visual analytics of these user behaviors for future research.

Original languageEnglish (US)
JournalIEEE Transactions on Big Data
DOIs
StateAccepted/In press - 2020

Keywords

  • Anomaly detection
  • Anomaly Detection
  • Big Data
  • Data visualization
  • Social networking (online)
  • Spatiotemporal phenomena
  • Taxonomy
  • User Behaviors
  • Visual analytics
  • Visual Analytics

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

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