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.

中文翻译：

---

探索用户行为数据以实现自适应网络安全

本文描述了对用户行为数据的预测分析可行性的探索性调查，作为开发自适应网络安全的有效用户模型的可能帮助。偏最小二乘结构方程模型通过收集用户对数字安全的态度的数据，并分析其如何影响他们对技术安全控制的采用和使用，应用于网络安全领域。然后应用贝叶斯网络建模将行为变量与来自网络浏览会话的模拟感官数据和/或日志以及收集的其他经验数据相结合，以支持个性化的自适应网络安全决策。实证研究的结果表明，在行为网络安全的背景下，预测分析是可行的，并且可以帮助生成有用的启发式方法，以设计和开发自适应网络安全机制。预测分析还有助于对数字安全行为知识进行编码，从而支持网络安全领域的操作适应和/或自动化。实验结果证明了应用于提取基于贝叶斯模型的输入数据的技术的有效性，以实现个性化自适应网络安全援助。