  Modern smart home control systems utilize real-time occupancy and activity
monitoring to ensure control efficiency, occupants' comfort, and optimal energy
consumption. Moreover, adopting machine learning-based anomaly detection models
(ADMs) enhances security and reliability. However, sufficient system knowledge
allows adversaries/attackers to alter sensor measurements through stealthy
false data injection (FDI) attacks. Although ADMs limit attack scopes, the
availability of information like occupants' location, conducted activities, and
alteration capability of smart appliances increase the attack surface.
Therefore, performing an attack space analysis of modern home control systems
is crucial to design robust defense solutions. However, state-of-the-art
analyzers do not consider contemporary control and defense solutions and
generate trivial attack vectors. To address this, we propose a control and
defense-aware novel attack analysis framework for a modern smart home control
system, efficiently extracting ADM rules. We verify and validate our framework
using a state-of-the-art dataset and a prototype testbed.
