Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids
Abdallah Alalem Albustami, Ahmad F. Taha, Elias Bou-Harb

TL;DR
This paper introduces novel stealthy false data injection attack strategies targeting nonlinear differential algebraic equation models of power grids, revealing vulnerabilities overlooked by simplified models and emphasizing the need for comprehensive cybersecurity approaches.
Contribution
It is the first to design and evaluate stealthy FDIAs against NDAE models of power networks, integrating dynamic and steady-state behaviors for more realistic threat assessment.
Findings
Attacks can evade both dynamic and static detection systems.
Coupling of dynamic and algebraic states restricts attack effectiveness.
Vulnerabilities exist in NDAE models that are absent in simplified models.
Abstract
Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats. This paper presents the first attempt to design and assess stealthy false data injection attacks (FDIAs) against nonlinear differential algebraic equation (NDAE) models of power networks. NDAE models, favored in industry for their ability to accurately capture both dynamic and steady-state behaviors, provide a more accurate representation of power system behavior by coupling dynamic and algebraic states. We propose novel FDIA strategies that simultaneously evade both dynamic and static intrusion detection systems while respecting…
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Taxonomy
TopicsSmart Grid Security and Resilience · Power System Optimization and Stability · Network Security and Intrusion Detection
