  Utility companies are increasingly leveraging residential demand flexibility
and the proliferation of smart/IoT devices to enhance the effectiveness of
residential demand response (DR) programs through automated device scheduling.
However, the adoption of distributed architectures in these systems exposes
them to the risk of false data injection attacks (FDIAs), where adversaries can
manipulate decision-making processes by injecting false data. Given the limited
control utility companies have over these distributed systems and data, the
need for reliable implementations to enhance the resilience of residential DR
schemes against FDIAs is paramount. In this work, we present a comprehensive
framework that combines DR optimisation, anomaly detection, and strategies for
mitigating the impacts of attacks to create a resilient and automated device
scheduling system. To validate the robustness of our framework against FDIAs,
we performed an evaluation using real-world data sets, highlighting its
effectiveness in securing residential DR systems.
