Cyber attack detection in smart grids: A survey of methods, challenges and future directions

Smart grids (SG) are the well-known cyber-physical systems responsible for processing, managing, controlling and optimising power energy for multi-scale organisations. Advancements in computing technologies have significantly revolutionised their performance with the integration of the Internet of Things (IoT), artificial intelligence (AI) and cloud technologies. However, such advancements also yield unforeseen vulnerabilities that pose serious threats to the grid management and control. In particular, the rapid advancement of AI and the increasing complexity of adversarial and other cyber attacks have created a highly vulnerable security landscape, despite years of countermeasure development. This study comprehensively reviews the recognition mechanisms utilised against such threats from the perspective of three layers of SG, such as sensing, communication and application layers. Additionally, it maps a wider range of cyber attacks with SG-related threat models and characteristics, along with vulnerable SG components. More importantly, it provides an in-depth comparative analysis of benchmark datasets and related evaluation metrics, including power system metrics, which is unique among other studies. This work also highlights the contemporary limitations and essential recommendations for future integration. Overall, the primary objective of the study is to guide scholars in understanding cutting-edge anomaly recognition techniques employed in SG and provide recommendations for mitigating challenges posed by advanced threats. • Mapping of smart grid-related cyber threat models, characteristics and component vulnerabilities. • Deeper analysis of smart grid-related cyber attack benchmark datasets and assesses the power system evaluation metrics. • Comprehensive analysis of recognition methods while highlighting deep learning based approaches for adversarial and emerging attacks. • Discussion of the contemporary challenges associated with these contexts and key recommendations.