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VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics

Nguyen, P. ORCID: 0000-0001-5643-0585, Henkin, R. ORCID: 0000-0002-5511-5230, Chen, S. , Andrienko, N. ORCID: 0000-0003-3313-1560, Andrienko, G. ORCID: 0000-0002-8574-6295, Thonnard, O. & Turkay, C. ORCID: 0000-0001-6788-251X (2019). VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics. IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2019.2934609


User behaviour analytics (UBA) systems offer sophisticated models that capture users’ behaviour over time with an aim to identify fraudulent activities that do not match their profiles. Making decisions based on such systems; however, requires an in-depth understanding of user behaviour both at an individual and at a group level where a group can consist of users with similar roles. We present a visual analytics approach to help analysts gain a comprehensive, multifaceted understanding of user behaviour at multiple levels. We take a user-centred approach to design a visual analytics framework supporting the analysis of collections of users and the numerous sessions of activities they conduct within digital applications. The framework is centred around the concept of hierarchical user profiles, where the profiles are built based on features derived from sessions they perform and visualised with task-informed designs to facilitate interactive exploration and investigation. We also present techniques to extract user tasks that summarise the behaviour and to cluster users according to these tasks for providing hierarchical overviews of groups of users along with individual users and the sessions they conduct. We externalise a series of analysis goals and tasks, and evaluate our methods through a number of use cases that demonstrate how these tasks are addressed. We observe that with the aid of interactive visual hierarchical user profiles, analysts were able to conduct exploratory and investigative analysis effectively, and able to understand the characteristics of user behaviour to make informed decisions whilst evaluating suspicious users and activities.

Publication Type: Article
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: hierarchical user profiles, user behaviour analytics, visual analytics, cybersecurity
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science > giCentre
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