Title: GEFA: a hybrid genetic-based ensemble forest mechanism for cybersecurity analysis of intelligent electronic devices in IoT-based smart grids

Authors: Zeng Lingcheng; An Yunzhu; Luo Qifeng; Lin Yuede; Zhou Heng

Addresses: Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd., Zhongshan, China ' School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong, China ' Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd., Zhongshan, China ' Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd., Zhongshan, China ' Zhongshan Power Supply Bureau of Guangdong Power Grid Co. Ltd., Zhongshan, China

Abstract: In recent years, smart grids have facilitated the integration of renewable energy sources (like solar and wind) and energy storage systems using Internet of Things (IoT) devices and smart applications. This integration helps balance supply and demand and improves grid resilience for Intelligent Electronic Devices (IEDs). Deploying the IEC-61850 standard for communication between Intelligent Electronic Devices (IEDs) indeed introduces new security challenges due to its specific architecture and communication protocols tailored for smart grids. With increased digital connectivity, smart grids implement robust cybersecurity measures to protect against cyber threats and ensure data privacy. Cybersecurity in smart grids and IoT is crucial due to the increasing digitalisation and connectivity of critical infrastructure. On the other hand, Machine Learning (ML) can play a crucial role in cybersecurity analysis of IEDs such as Intrusion Detection Systems (IDS) in smart grids by leveraging data analytics and pattern recognition techniques. In this paper, a Genetic-based Ensemble Forest Algorithm (GEFA) is presented to predict the attack surface for cyber threats of IEDs in IoT-based smart grids. Also, a feature selection method based on Ant Colony Optimisation (ACO) algorithm is applied for a real public power system data set to enhance the performance of prediction procedure. The experimental results show that the suggested hybrid ACO-GETA approach outperforms other prediction approaches to achieve highest accuracy and F1-Score with 100%.

Keywords: IoT; internet of things; smart grids; IDS; intrusion detection system; machine learning; genetic algorithm; ACO; feature selection.

DOI: 10.1504/IJGUC.2026.154454

International Journal of Grid and Utility Computing, 2026 Vol.17 No.3, pp.262 - 274

Received: 28 Aug 2024
Accepted: 08 Jan 2025
Published online: 29 Jun 2026 *

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