Protection Against Graph-Based False Data Injection Attacks on Power Systems
Gal Morgenstern, Jip Kim, James Anderson, Gil Zussman, and Tirza, Routtenberg

TL;DR
This paper introduces a novel graph-based false data injection attack on power systems and proposes a protection scheme that enhances detection, demonstrating effectiveness through simulations on standard test cases.
Contribution
It develops a new GFDI attack model minimizing graph total variation and proposes a minimal measurement protection scheme to improve detection capabilities.
Findings
GFDI attack can bypass existing detectors by minimizing graph TV.
Protection scheme reduces hardware costs while maintaining detection.
Simulations on IEEE test cases validate the approach's effectiveness.
Abstract
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology identification using the representation of the power system voltages as smooth graph signals. Within this framework, efficient methods have been developed for detecting false data injection (FDI) attacks, which until now were perceived as non-smooth with respect to the graph Laplacian matrix. Consequently, these methods may not be effective against smooth FDI attacks. In this paper, we propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph total variation (TV) under practical constraints. We present the GFDI attack as the solution for a non-convex constrained optimization problem. The solution to the GFDI attack problem is…
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Taxonomy
TopicsSmart Grid Security and Resilience · Network Security and Intrusion Detection · Adversarial Robustness in Machine Learning
MethodsTest
