HomeGuardian: Detecting Anomaly Events in Smart Home Systems

Abstract

As a typical application of Internet of Things (IoT), home automation systems, namely, smart homes, provide a more convenient and intelligent life experience through event recognition, automation control, and remote device access. However, smart home systems have also given rise to new complications for security issues. As an event-driven IoT system, smart home environments are vulnerable to security attacks, and vulnerable devices are far-spread due to the quick development cycles. Attack vectors to smart homes inevitably manifest in abnormal event contexts. In this paper, we propose HomeGuardian, a context-based approach to identify abnormal events in smart homes. In our approach, we extract temporal context and environmental context from system logs, aggregate (embed) these hybrid contexts, and construct a learning-based classifier to identify the abnormal events. We develop a testbed to implement and evaluate our approach.