False Data Injection Attacks in Smart Grids: State of the Art and Way Forward

TL;DR

This paper addresses False Data Injection (FDI) attacks in smart grids, emphasizing the need for joint detection and localization since FDI can bypass traditional Bad Data Detection. It systematically reviews over 40 contributions, organizing them into data-driven and mathematical-modeling approaches, and analyzes localization techniques, evaluation setups, attacker knowledge, and attack types. The survey finds that data-driven methods, particularly graph-based and temporal neural networks, dominate localization work, but real-world ground truth datasets and deployments remain scarce, limiting practical adoption. It outlines open issues and future directions—real-time online detection, scalable testing on larger grids, adversarial robustness, and data fusion for robust localization—to advance smart-grid security and reliability.

Abstract

In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and industry FDI attacks can affect the internal state estimation processcritical for smart grid monitoring and controlthus being able to bypass conventional Bad Data Detection BDD methods Hence prompt detection and precise localization of FDI attacks is becomming of paramount importance to ensure smart grids security and safety Several papers recently started to study and analyze this topic from different perspectives and address existing challenges Datadriven techniques and mathematical modelings are the major ingredients of the proposed approaches The primary objective of this work is to provide a systematic review and insights into FDI attacks joint detection and localization approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localization aspects For this purpose we select and inspect more than forty major research contributions while conducting a detailed analysis of their methodology and objectives in relation to the FDI attacks detection and localization We provide our key findings of the identified papers according to different criteria such as employed FDI attacks localization techniques utilized evaluation scenarios investigated FDI attack types application scenarios adopted methodologies and the use of additional data Finally we discuss open issues and future research directions

Figures (7)

• Figure 1: Architecture of a traditional electrical power grid.
• Figure 2: Conceptual model of a smart grid.
• Figure 3: Possible attack sources in a smart grid cardenas2019cyber.
• Figure 4: State transitions of a generic power systems
• Figure 5: Number of papers related to FDI attacks