Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects

In modern Smart Grids (SGs) ruled by advanced computing and networking technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this connection, a portion of transported data, containing confidential information, must be protected as it is vulnerable and subject to several cyber threats. SG cyberspace adversaries attempt to gain access through networking platforms to commit several criminal activities such as disrupting or malicious manipulation of whole electricity delivery process including generation, distribution, and even customer services such as billing, leading to serious damage, including financial losses and loss of repu-tation. Therefore, human awareness training and software technologies are necessary precautions to ensure the reliability of data traffic and power transmission. By exploring the available literature, it is undeniable that Machine Learning (ML) has become the latest in the timeline and one of the leading artificial intelligence technologies capable of detecting, identifying, and responding by mitigating adversary attacks in SGs. In this context, the main objective of this paper is to review different ML tools used in recent years for cyberattacks analysis in SGs. It also provides important guidelines on ML model selection as a global solution when building an attack predictive model. A detailed classification is therefore developed with respect to data security triad, i. e., Confidentiality, Integrity, and Availability (CIA) within different types of cyber threats, systems, and datasets. Furthermore, this review highlights the various encountered challenges, drawbacks, and possible solutions as future prospects for ML cybersecurity applications in SGs.

Automated summary

This paper reviews different machine learning tools used in recent years for analyzing cyberattacks in smart grids and provides important guidelines for a global solution. The paper provides a detailed classification of various types of cyber threats, systems, and datasets, highlighting the challenges, drawbacks, and future prospects for machine learning cybersecurity applications in smart grids.