Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversar...

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Bibliographic Details
Published in:IEEE transactions on artificial intelligence Vol. 5; no. 12; pp. 6601 - 6616
Main Authors: Takiddin, Abdulrahman, Ismail, Muhammad, Atat, Rachad, Serpedin, Erchin
Format: Journal Article
Language:English
Published: IEEE 01.12.2024
Subjects:
Accuracy
Artificial intelligence
Cyber-physical systems security
cyberattacks
Detectors
evasion attacks
false data injection attacks (FDIAs)
graph autoencoder
graph neural networks (GNNs)
machine learning
Robustness
Smart grids
Topology
Training
ISSN:2691-4581, 2691-4581
Online Access:Get full text
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Summary:Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR compared to a random selection; 2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%-13% higher degradation in DR compared to traditional FDIAs; and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5<inline-formula><tex-math notation="LaTeX">-</tex-math></inline-formula>53% compared to benchmark detectors against FDIEAs.
ISSN:2691-4581
2691-4581
DOI:10.1109/TAI.2024.3464511