  Smart home IoT systems and devices are susceptible to attacks and
malfunctions. As a result, users' concerns about their security and safety
issues arise along with the prevalence of smart home deployments. In a smart
home, various anomalies (such as fire or flooding) could happen, due to cyber
attacks, device malfunctions, or human mistakes. These concerns motivate
researchers to propose various anomaly detection approaches. Existing works on
smart home anomaly detection focus on checking the sequence of IoT devices'
events but leave out the temporal information of events. This limitation
prevents them to detect anomalies that cause delay rather than
missing/injecting events. To fill this gap, in this paper, we propose a novel
anomaly detection method that takes the inter-event intervals into
consideration. We propose an innovative metric to quantify the temporal
similarity between two event sequences. We design a mechanism to learn the
temporal patterns of event sequences of common daily activities. Delay-caused
anomalies are detected by comparing the sequence with the learned patterns. We
collect device events from a real-world testbed for training and testing. The
experiment results show that our proposed method achieves accuracies of 93%,
88%, 89% for three daily activities.
