Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals

TL;DR

This paper introduces a novel anomaly detection method for smart home IoT systems that incorporates event time intervals, enabling detection of delay-based anomalies often missed by sequence-only approaches.

Contribution

It proposes a new temporal similarity metric and a learning mechanism for daily activity patterns, effectively detecting delay-related anomalies in smart home environments.

Findings

Achieved 93%, 88%, 89% accuracy on three daily activities

Effectively detects delay-caused anomalies missed by previous methods

Utilizes real-world testbed data for validation

Abstract

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.