Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts

Abstract

Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the Internet of Things devices (such as smart metres 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 culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical 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 1) adversarial models, 2) attack targets, and 3) impacts in the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid. Related literature are analysed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack research is identified, and a number of future research directions is recommended.

arXiv commentDouble-column of 24 pages, prepared based on IEEE Transaction article

References in corpus (4)

• Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
• Subspace Methods for Data Attack on State Estimation: A Data Driven Approach
• Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models
• Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach

Cited by in corpus (4)

• False Data Injection Attacks in Smart Grids: State of the Art and Way Forward
• Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes
• A Survey of the Metrics, Uses, and Subjects of Diversity-Based Techniques in Software Testing
• Short-Term Forecasting of Energy Production and Consumption Using Extreme Learning Machine: A Comprehensive MIMO based ELM Approach