Device-level security has become a major concern in smart home systems. Detecting problems in smart home sytems strives to increase accuracy in near real time without hampering the regular tasks of the smart home. The current state of the art in detecting anomalies in smart home devices is mainly focused on the app level, which provides a basic level of security by assuming that the devices are functioning correctly. However, this approach is insufficient for ensuring the overall security of the system, as it overlooks the possibility of anomalies occurring at the lower layers such as the devices. In this article, we propose a novel notion, correlated graph, and with the aid of that, we develop our system to detect misbehaving devices without modifying the existing system. Our correlated graphs explicitly represent the contextual correlations among smart devices with little knowledge about the system. We further propose a linkage path model and a sensitivity ranking method to assist in detecting the abnormalities. We implement a semi-automatic prototype of our approach, evaluate it in real-world settings, and demonstrate its efficiency, which achieves an accuracy of around 90% in near real time.

设备级安全已成为智能家居系统的主要关注点。检测智能家居系统中的问题努力在不妨碍智能家居的常规任务的情况下近乎实时地提高准确性。检测智能家居设备异常的当前技术水平主要集中在应用程序级别，它通过假设设备正常运行来提供基本级别的安全性。但是，这种方法不足以确保系统的整体安全性，因为它忽略了设备等较低层发生异常的可能性。在这篇文章中，我们提出了一个新的概念，相关图，并借助于此，我们开发了我们的系统来检测行为不端的设备，而无需修改现有系统。我们的相关图明确表示了对系统知之甚少的智能设备之间的上下文相关性。我们进一步提出了一种链接路径模型和一种灵敏度排序方法来帮助检测异常。我们实现了我们方法的半自动原型，在真实环境中对其进行评估，并展示其效率，近乎实时地实现了约 90% 的准确率。