Work

A comprehensive review of anomaly detection in smart grids: A machine learning approach to cyber threat identification

Year: 2026

Type: article

Abstract: Security of smart grids (SG) has gained more attention in the past decades as one of the crucial problems. Implementing Machine learning (ML) has been the most capable solution for strengthening cyber security in SGs. Considering the significance of ML for anomaly detection, this review paper investigates the role of ML models in ensuring SG security. Various research works have discussed the issue of SG security and have reviewed different technologies used including the deployment of ML for attack detection in SG. However, the continuous evolution of cyber threats in the dynamic SG environment motivates this review to contribute to the existing works. This review discusses the types of anomalies and cyber threats which threaten the security of SG. In addition, this review examines the role of different ML models along with datasets used for training ML models. On the basis of the study, this paper identifies the challenges and research gaps and outline possible future research directions.

Source: Energy Reports

DOI: 10.1016/j.egyr.2026.109668

Authors: Justin Baby, A. Immanuel Selvakumar

Institution: Karunya University

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,040