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Exploring user behavioral data for adaptive cybersecurity

机译：探索自适应网络安全的用户行为数据

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摘要

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.
机译：本文介绍了对用户行为数据的预测分析可行性作为可能辅助的探索性调查，以实现自适应网络安全的有效用户模型。部分最小二乘结构方程建模应用于网络安全领域，通过收集用户对数字安全的态度数据，并分析了如何影响技术安全控制的采用和使用。然后应用贝叶斯网络建模以将行为变量与模拟感官数据和/或从Web浏览会话的日志集成，以及收集的其他经验数据以支持个性化的自适应网络安全决策。实证研究结果表明，预测分析在行为网络安全的背景下是可行的，并且可以帮助产生自适应网络安全机制的设计和开发的有用启发式机制。预测分析还可以帮助编码数字安全行为知识，该知识可以支持网络安全领域的操作的适应和/或自动化。实验结果表明，适用于提取基于贝叶斯的模型的输入数据的技术的有效性，以进行个性化的自适应网络安全援助。

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