False Data Injection Attacks Against Distributed Residential Demand Response Systems in Smart Grids

The introduction of the smart grid allows utility companies to leverage the residential demand flexibility in implementing effective demand response systems. Distributed architectures and the heterogeneous nature of residential DR systems allow malicious individuals and organised groups to execute different cyberattacks. This thesis analyses novel false data injection attacks (FDIAs) and their potential impact on these systems. Further, the use of anomaly detection methods in the context of FDIA detection is analysed. Moreover, the use of anomaly detection methods and impact alleviation techniques in improving the resiliency of DR schemes to ensure the effectiveness of distributed DR schemes is analysed.