Data Attacks on Power Grids: Leveraging Detection

TL;DR

This paper introduces a novel data attack model on power grid measurements that can bypass detection, reducing attack size and targeting resilient systems, with algorithms validated on IEEE test systems.

Contribution

The paper proposes a new data attack model that can succeed despite detection, with algorithms for attack construction and analysis of attack conditions.

Findings

Reduced minimum attack size compared to undetectable attacks

Algorithms effectively construct attack vectors in polynomial time

Simulations demonstrate attack success on IEEE test systems

Abstract

Data attacks on meter measurements in the power grid can lead to errors in state estimation. This paper presents a new data attack model where an adversary produces changes in state estimation despite failing bad-data detection checks. The adversary achieves its objective by making the estimator incorrectly identify correct measurements as bad data. The proposed attack regime's significance lies in reducing the minimum sizes of successful attacks to more than half of that of undetectable data attacks. Additionally, the attack model is able to construct attacks on systems that are resilient to undetectable attacks. The conditions governing a successful data attack of the proposed model are presented along with guarantees on its performance. The complexity of constructing an optimal attack is discussed and two polynomial time approximate algorithms for attack vector construction are developed. The performance of the proposed algorithms and efficacy of the hidden attack model are demonstrated through simulations on IEEE test systems.