Leveraging Convex Relaxation to Identify the Feasibility of Conducting AC False Data Injection Attack in Power Systems
Mohammadreza Iranpour, Mohammad Rasoul Narimani

TL;DR
This paper introduces a convex relaxation approach to determine the feasibility of AC false data injection attacks in power systems, addressing challenges posed by nonlinear power flow equations.
Contribution
It proposes a QC relaxation-based method to assess attack feasibility, distinguishing infeasibility due to system constraints from solver limitations.
Findings
Effective in identifying infeasible attack scenarios
Applied successfully to IEEE 118-bus system
Provides insights into attackable regions in power networks
Abstract
FDI (False Data Injection) attacks are critical to address as they can compromise the integrity and reliability of data in cyber-physical systems, leading to potentially severe consequences in sectors such as power systems. The feasibility of FDI attacks has been extensively studied from various perspectives, including access to measurements and sensors, knowledge of the system, and design considerations using residual-based detection methods. Most research has focused on DC-based FDI attacks; however, designing AC FDI attacks involves solving a nonlinear optimization problem, presenting additional challenges in assessing their feasibility. Specifically, it is often unclear whether the infeasibility of some designed AC FDI attacks is due to the nonconvexity and nonlinearity inherent to AC power flows or if it stems from inherent infeasibility in specific cases, with local solvers…
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