  Our everyday lives are expanding fast with the introduction of new Smart Home
Systems (SHSs). Today, a myriad of SHS devices and applications are widely
available to users and have already started to re-define our modern lives.
Smart home users utilize the apps to control and automate such devices. Users
can develop their own apps or easily download and install them from
vendor-specific app markets. App-based SHSs offer many tangible benefits to our
lives, but also unfold diverse security risks. Several attacks have already
been reported for SHSs. However, current security solutions consider smart home
devices and apps individually to detect malicious actions rather than the
context of the SHS as a whole. The existing mechanisms cannot capture user
activities and sensor-device-user interactions in a holistic fashion. To
address these issues, in this paper, we introduce Aegis, a novel context-aware
security framework to detect malicious behavior in a SHS. Specifically, Aegis
observes the states of the connected smart home entities (sensors and devices)
for different user activities and usage patterns in a SHS and builds a
contextual model to differentiate between malicious and benign behavior. We
evaluated the efficacy and performance of Aegis in multiple smart home settings
(i.e., single bedroom, double bedroom, duplex) with real-life users performing
day-to-day activities and real SHS devices. We also measured the performance of
Aegis against five different malicious behaviors. Our detailed evaluation shows
that 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 minimum
overhead in detecting malicious behavior in SHS, ensuring easy deployability in
real-life smart environments.
