# Visual Analytics of Anomalous User Behaviors: A Survey

arXiv: https://arxiv.org/abs/1905.06720 · HTML: https://mlsanity.com/paper/1905.06720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 4 of 4)

- [AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems](https://mlsanity.com/paper/2402.08995/) (2024)
- [From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management](https://mlsanity.com/paper/2304.03368/) (2023)
- [Novelty Detection in Sequential Data by Informed Clustering and Modeling](https://mlsanity.com/paper/2103.03943/) (2021)
- [Challenges in anomaly and change point detection](https://mlsanity.com/paper/2212.13520/) (2022)

## Abstract (arXiv)

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.