Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts

Smart Grid is organically growing over the centrally controlled power system and becoming a massively interconnected cyber-physical system with advanced technologies of fast communication and intelligence (such as Internet of Things, smart meters, and intelligent electronic devices). While the convergence of a significant number of cyber-physical elements has enabled the Smart Grid to be far more efficient and competitive in addressing the growing global energy challenges, it has also introduced a large number of vulnerabilities in the cyber-physical space culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical types of cyberattacks, and appears to be a focal point of interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the FDI attacks, with particular emphasis on adversarial models, attack targets, and impacts on the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entir e spectrum of the Smart Grid . Related literature are analyzed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a vast range of technical limitations of existing false data attack research is identified, and a number of futu r e research directions is recommended.

Automated summary

This paper provides a comprehensive review of the recent advances in false data injection attacks, with a particular focus on adversarial models, attack targets, and impacts on the Smart Grid infrastructure. It aims to offer a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid.