Deep learning for cybersecurity in smart grids: Review and perspectives - Jiaqi Ruan; Gaoqi Liang; Junhua Zhao; Huan Zhao; Jing Qiu; Fushuan Wen; Zhao Yang Dong (Energy Conversion and Economics)
Abstract Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence, particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.