A Subgrid-Oriented Privacy-Preserving Microservice Framework Based on Deep Neural Network for False Data Injection Attack Detection in Smart Grids

Abstract

False data injection attacks (FDIAs) have recently become a major threat to smart grids. Most of the existing FDIAS detection methods have focused on modeling the temporal relationship of time-series measurement data, but have paid less attention to the spatial relationship between bus/line measurement data, and have failed to consider the relationship between sub-grids. To address these issues, we propose a sub-grid-oriented microservice framework by integrating a well-designed spatialtemporal neural network for FDIA detection in AC-model power systems. First, a well-designed neural network was developed to model the spatial-temporal relationship of bus/line measurements for sub-grids. A microservice-based supervising network is then proposed for integrating the representation features obtained from sub-grids for the collaborative detection of FDIAs. To evaluate the proposed framework, three types of FDIA datasets are generated based on a public benchmark power grid. Case studies on the FDIA datasets show that our method outperforms state-of-the-art methods for FDIA detection in these datasets.

PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-Model Transmission Systems

“…Another one is that state variables estimated from false measurements may be incorrect to the power grids at the current time. Recently, Yin et al [8] proposed a sub-grid-oriented microservice-based supervising network through privacy-preserving collaborative learning to detect SFDIAs. However, this method mainly considered the local spatiotemporal relationship of measurement in sub-grids in the privacy-preserving setting.…”

“…In this subsection, we provide the details about the organization of the measurements. Common measurement variables and notations such as in [8], [19], [22] are adopted. Typically, the measurements for power systems include line measurements and bus measurements.…”

“…I N smart grids, a stealthy false data injection attack (SF-DIA), which is aimed at maliciously manipulating measurements in a smart grid and may lead to serious consequences for the power system [1]- [3], has recently become a focus on smart grid research [4]- [8]. Contrary to other cyberattacks (such as jamming and distributed denial-of-service), studies have proved that well-defined SFDIA measurements can bypass traditional bad measurement detection mechanisms [5], [9], as the attacks obey power equations.…”

PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-model Transmission Systems

“…The generation of the SFDIA measurement is based on the method proposed in [5], [8]. The attacks were launched on a target bus by maliciously modifying either its voltage angle (V a) or voltage magnitude (V m) [5], [8].…”

“…The generation of the SFDIA measurement is based on the method proposed in [5], [8]. The attacks were launched on a target bus by maliciously modifying either its voltage angle (V a) or voltage magnitude (V m) [5], [8]. To comprehensively evaluate the performance of the SFDIA detection, three types of SFDIAs are designed in this experiment, summarized as follows:…”