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This paper describes an exploratory investigation into the feasibility of predictive analytics of user behavioural data as a possible aid in developing effective user models for adaptive cybersecurity. Partial Least Squares Structural Equation Modelling (PLS-SEM) is applied to the domain of cybersecurity by collecting data on users' attitude towards digital security, and analysing how that influences their adoption and usage of technological security controls. Bayesian-network modeling is then applied to integrate the behavioural variables with simulated sensory data from the web browser 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 behavioural 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 behavioural 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.

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