Detecting False Data Injection Attacks in Smart Grids with Modeling Errors: A Deep Transfer Learning Based Approach

Abstract

Most traditional false data injection attack (FDIA) detection approaches rely on static system parameters or a single known snapshot of dynamic ones. However, such a setting significantly weakens the practicality of these approaches when facing the fact that the system parameters are dynamic and cannot be accurately known during operation due to the presence of uncertainties in practical smart grids. In this paper, we propose an FDIA detection mechanism from the perspective of transfer learning. Specifically, the known initial/approximate system is treated as a source domain, which provides abundant simulated normal and attack data. The real world's unknown running system is taken as a target domain where sufficient real normal data are collected for tracking the latest system states online. The designed transfer strategy that aims at making full use of data in hand is divided into two optimization stages. In the first stage, a deep neural network (DNN) is built by simultaneously optimizing several well-designed terms with both simulated data and real data, and then it is fine-tuned via real data in the second stage. Several case studies on the IEEE 14-bus power system verify the effectiveness of the proposed mechanism.

Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids

“…However, this work does not expand on larger bus systems and the review of literature also suggests that the development of unsupervised techniques for FDIA detection is still in the initial stages of research. Xu et al [18] utilized transfer learning to consider the dynamic nature of the real-world transmission line parameters. The simulated data on the IEEE-14 and IEEE-118 bus system was considered as the source domain and the power system, with real-world variation in transmission line parameters, was considered as the target domain.…”

Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering

“…This success is partly due to their capacity to automate complex tasks that are typically performed by humans and are difficult to program. It is also driven by the high performance they have achieved in many important fields regarding perception and decision-making tasks in smart grids [25,64], networked surveillance [9,60], medical imaging [6,48], and autonomous vehicles [31,59]. A good example of the latter is IEE [20], our industry partner in this research, who is extending its portfolio of in-vehicle monitoring systems with DNN-based products.…”