Detecting false data attacks using machine learning techniques in smart grid: A survey. (15th November 2020) - Cui, Lei; Qu, Youyang; Gao, Longxiang; Xie, Gang; Yu, Shui
Abstract: The big data sources in smart grid (SG) enable utilities to monitor, control, and manage the energy system effectively, which is also promising to advance the efficiency, reliability, and sustainability of energy usage. However, false data attacks, as a major threat with wide targets and severe impacts, have exposed the SG systems to a large variety of security issues. To detect this threat effectively, several machine learning (ML)-based methods have been developed in the past few years. In this paper, we provide a comprehensive survey of these advances. The paper starts by providing a brief overview of SG architecture and its data sources. Moreover, the categories of false data attacks followed by data security requirements are introduced. Then, the recent ML-based detection techniques are summarized by grouping them into three major detection scenarios: non-technical losses, state estimation, and load forecasting. At last, we further investigate the potential research directions at the end of the paper, considering the deficiencies of current ML-based mechanisms. Specifically, we discuss intrusion detection against adversarial attacks, collaborative and decentralized detection framework, detection with privacy preservation, and some potential advanced ML techniques. Highlights: A comprehensive overview of security concerns caused by false data attacks in smart grid. A detailed taxonomy of machine learning-based countermeasures against false data attacks. A discussion on vulnerabilities of current machine learning-based methods and future directions.