Exploring user behavioral data for adaptive cybersecurity

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

Abstract

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.

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. https://doi.org/10.1007/s11257-019-09236-5

• Journal Article Type: Article
• Acceptance Date: Apr 12, 2019
• Online Publication Date: May 4, 2019
• Publication Date: Jul 1, 2019
• Deposit Date: Jul 11, 2019
• Publicly Available Date: May 5, 2020
• 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
• DOI: https://doi.org/10.1007/s11257-019-09236-5
• Keywords: Human-Computer Interaction; Education; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/2299181
• Publisher URL: https://link.springer.com/article/10.1007%2Fs11257-019-09236-5
• 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: http://dx.doi.org/10.1007%2Fs11257-019-09236-5
• Contract Date: Jul 11, 2019