Data Attacks on Power Grids: Leveraging Detection
This addresses a critical security vulnerability in power grid infrastructure, offering a novel approach to bypass existing detection mechanisms.
The paper tackles the problem of data attacks on power grid meter measurements that evade bad-data detection, resulting in state estimation errors. It introduces a new attack model that reduces the minimum attack size by more than half compared to undetectable attacks and can target systems resilient to such attacks.
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.