Designing Sparse AC False Data Injection Attack

TL;DR

This paper presents a novel approach to designing sparse false data injection attacks on AC power systems, formulating it as a complex optimization problem and demonstrating its effectiveness through simulations.

Contribution

It introduces a mixed-integer nonlinear programming framework for sparse AC FDI attacks, addressing the challenge of minimal measurement manipulation while maintaining nonlinearity.

Findings

Effective attack with minimal measurements on IEEE 57-bus system

Optimization approach successfully balances attack impact and detectability

Demonstrates feasibility of sparse AC FDI attacks in practice

Abstract

False Data Injection (FDI) attacks pose significant threats by manipulating measurement data, leading to incorrect state estimation. Although numerous studies have focused on designing DC FDI attacks, few have addressed AC FDI attacks due to the complexity of incorporating non-linear AC power flows in the design process. Additionally, designing a sparse AC FDI attack presents another challenge because it involves solving a mixed-integer nonlinear programming problem with nonconvex constraints, which is inherently difficult. This paper explores the design and implementation of a sparse AC FDI attack, where the attacker strategically selects a minimal set of measurements to manipulate while maintaining the nonlinearity and interdependence of AC power flow equations. The objective is to minimize the number of altered measurements, thereby reducing the attack's detectability while achieving the desired state estimation error. The problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem. Binary variables indicate the selection of measurements to be manipulated, and continuous variables represent the measurement values. An optimization problem is designed to minimize the number of binary variables, translating into a sparse attack, while ensuring the attack remains efficient and hard to detect. The big-M method and conditional constraints are utilized to handle the fixed and variable measurement parameters effectively. Simulation results on the standard IEEE 57-bus test system demonstrate the efficacy of the sparse AC FDI attack in terms of its impact on state estimation and the minimal number of measurements required for successful implementation.