False Data Injection Attacks Based on Least Squares Generative Adversarial Networks with Reconstruction Loss

Cybersecurity is increasingly vital in power systems, particularly with the rise of Internet of Things (IoT) devices. The integration of these devices amplifies the system’s exposure to threats like False Data Injection Attacks (FDIA). This work proposes a Generative Adversarial Networks (GANs) framework for generating FDIA against power system state estimation from the attacker’s perspective. Specifically, we propose Least Squares Generative Adversarial Networks with Reconstruction Loss (LSGAN-RL) FDIA to produce artificial measurements that pass the traditional bad data detection (BDD) mechanism. One advantage of LSGAN-RL FDIA is that it only uses the historical measurement data without knowledge of the system topology and line impedance. This algorithm adeptly mirrors the data distribution seen in power system measurements. Our simulations on the IEEE 14-bus system present a 99.31% pass rate of the crafted data evading BDD. The kernel density plot between the actual and generated measurements further underscores the effectiveness of LSGAN-RL in emulating stealthy FDIAs.

I. Introduction

Power systems play an essential role in the present and future world. Due to the increasing power demand, there is a need to facilitate more efficient and cost-effective power system designs for real-world applications [1]. With its high automation and operational capabilities, the IoT has become an indispensable device in power plants. However, this kind of automation provides an accessible entry for potential attackers to compromise the power system, resulting in severe economic and physical social consequences. In December 2015, the power system in Ukraine was attacked, leading to a significant power equipment outage that paralyzed parts of the country for several hours [2]. This event is the first publicly acknowledged incident. Since Ukraine’s blackout, cyber attacks and defense in the power grid gradually become a research topic [3].