Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility towards securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim to overcome these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

中文翻译：

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智能家居中基于知识的网络物理安全：回顾


智能家居系统代表了现代建筑基础设施的未来，因为它们集成了众多设备和应用程序以提高整体生活质量。这些系统在智能设备之间建立连接，利用网络技术和算法控制来监视和管理物理环境。然而，确保智能家居的强大安全性以及保护智能设备的安全是一项艰巨的挑战。智能家居的大量安全解决方案依赖于数据驱动的方法（例如机器/深度学习）来识别和减轻潜在威胁。这些方法涉及在广泛的数据集上训练模型，这将它们与知识驱动的方法区分开来。在这篇评论中，我们研究了知识在智能家居中的作用，重点是对各种事件的理解和推理及其对保护智能家居的效用。我们提出了一种分类法来描述决策方法的分类。通过指定最常见的漏洞、攻击和威胁，我们可以分析和评估针对它们的对策。我们还研究了已审查的文献中如何评估智能家居。此外，我们还探讨了智能家居固有的挑战，并研究了旨在克服这些限制的现有解决方案。最后，我们研究了智能家居安全研究的关键差距，并定义了知识驱动方案的未来研究方向。