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Exploring user behavioral data for adaptive cybersecurity

Addae, Joyce H.; Sun, Xu; Towey, Dave; Radenkovic, Milena


Joyce H. Addae

Xu Sun

Dave Towey

Milena Radenkovic


This paper describes an exploratory investigation into the feasibility of predictive analytics of user behavioral data as a possible aid in developing effective user models for adaptive cybersecurity. Partial least squares structural equation modeling is applied to the domain of cybersecurity by collecting data on users’ attitude towards digital security, and analyzing how that influences their adoption and usage of technological security controls. Bayesian-network modeling is then applied to integrate the behavioral variables with simulated sensory data and/or logs from a web browsing session and other empirical data gathered to support personalized adaptive cybersecurity decision-making. Results from the empirical study show that predictive analytics is feasible in the context of behavioral cybersecurity, and can aid in the generation of useful heuristics for the design and development of adaptive cybersecurity mechanisms. Predictive analytics can also aid in encoding digital security behavioral knowledge that can support the adaptation and/or automation of operations in the domain of cybersecurity. The experimental results demonstrate the effectiveness of the techniques applied to extract input data for the Bayesian-based models for personalized adaptive cybersecurity assistance.

Journal Article Type Article
Publication Date Jul 1, 2019
Journal User Modeling and User-Adapted Interaction
Print ISSN 0924-1868
Electronic ISSN 1573-1391
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 29
Issue 3
Pages 701-750
Institution Citation Addae, J. H., Sun, X., Towey, D., & Radenkovic, M. (2019). Exploring user behavioral data for adaptive cybersecurity. User Modeling and User-Adapted Interaction, 29(3), 701-750. doi:10.1007/s11257-019-09236-5
Keywords Human-Computer Interaction; Education; Computer Science Applications
Publisher URL
Additional Information This is a post-peer-review, pre-copyedit version of an article published in User Modeling and User-Adapted Interaction. The final authenticated version is available online at:


This file is under embargo until May 4, 2020 due to copyright restrictions.

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