Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks

Abstract: 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.