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Our everyday lives are expanding fast with the introduction of new Smart HomeSystems (SHSs). Today, a myriad of SHS devices and applications are widelyavailable to users and have already started to re-define our modern lives.Smart home users utilize the apps to control and automate such devices. Userscan develop their own apps or easily download and install them fromvendor-specific app markets. App-based SHSs offer many tangible benefits to ourlives, but also unfold diverse security risks. Several attacks have alreadybeen reported for SHSs. However, current security solutions consider smart homedevices and apps individually to detect malicious actions rather than thecontext of the SHS as a whole. The existing mechanisms cannot capture useractivities and sensor-device-user interactions in a holistic fashion. Toaddress these issues, in this paper, we introduce Aegis, a novel context-awaresecurity framework to detect malicious behavior in a SHS. Specifically, Aegisobserves the states of the connected smart home entities (sensors and devices)for different user activities and usage patterns in a SHS and builds acontextual model to differentiate between malicious and benign behavior. Weevaluated the efficacy and performance of Aegis in multiple smart home settings(i.e., single bedroom, double bedroom, duplex) with real-life users performingday-to-day activities and real SHS devices. We also measured the performance ofAegis against five different malicious behaviors. Our detailed evaluation showsthat Aegis can detect malicious behavior in SHS with high accuracy (over 95%)and secure the SHS regardless of the smart home layout, device configuration,installed apps, and enforced user policies. Finally, Aegis achieves minimumoverhead in detecting malicious behavior in SHS, ensuring easy deployability inreal-life smart environments.

Aegis: A Context-aware Security Framework for Smart Home Systems