Muganji Julius, Bainomugisha Engineer
Department of Computer Science, Makerere University, Kampala, Uganda.
SN Comput Sci. 2022;3(6):450. doi: 10.1007/s42979-022-01293-9. Epub 2022 Aug 20.
Compared to traditional user authentication methods, continuous user authentication (CUA) provide enhanced protection, guarantees against unauthorized access and improved user experience. However, developing effective continuous user authentication applications using the current programming languages is a daunting task mainly because of lack of abstraction methods that support continuous user authentication. Using the available language abstractions developers have to write the CUA concerns (e.g., extraction of behavioural patterns and manual checks of user authentication) from scratch resulting in unnecessary software complexity and are prone to error. In this paper, we propose new language features that support the development of applications enhanced with continuous user authentication. We develop Plascua, a continuous user authentication language extension for event detection of user bio-metrics, extracting of user patterns and modelling using machine learning and building user authentication profiles. We validate the proposed language abstractions through implementation of example case studies for CUA.
与传统的用户认证方法相比，持续用户认证（CUA）提供了增强的保护，防止未经授权的访问，并改善了用户体验。然而，使用当前的编程语言开发有效的持续用户认证应用程序是一项艰巨的任务，主要是因为缺乏支持持续用户认证的抽象方法。使用现有的语言抽象，开发人员必须从头开始编写CUA相关内容（例如，行为模式的提取和用户认证的手动检查），这会导致不必要的软件复杂性，并且容易出错。在本文中，我们提出了新的语言特性，以支持开发增强了持续用户认证功能的应用程序。我们开发了Plascua，这是一种用于用户生物特征事件检测、用户模式提取、机器学习建模以及构建用户认证配置文件的持续用户认证语言扩展。我们通过为CUA实施示例案例研究来验证所提出的语言抽象。
