Work

CAD-IGMS: Cyber attack detection in an intelligent grid management system via the deep learning enhancing grid reliability

Year: 2025

Type: article

Abstract: Cyber security (CS) has emerged as a critical component in Intelligent Grid Management Systems (IGMS) due to the integration of Supervisory Control and Data Acquisition (SCADA) systems in modern power grids. However, this increased dependence on connectivity and technology leaving SG communication networks vulnerable to widespread attacks. To address these challenges, a novel Cyber Attack Detection system for IGMS (CAD-IGMS) using deep learning has been proposed. Renewable energy sources feed power through converters to a Load Source Inverter (LSI), with SCADA handling data acquisition and control. Features are extracted using a Cascaded Autoencoder and a Self-Attention based Convolutional Neural Network (SACNN) is utilized to detect and classify cyber-attacks in smart grids. Implemented in MATLAB, CAD-IGMS demonstrates superior performance, achieving accuracy improvements of 15.8 %, 17.2 % , and 20.6 % over Artificial Feed-forward Network (AFN), Genetically Seeded Flora Transformer Neural Network (GSFTNN), and Convolutional Neural Network based Long short term memory (CNN-LSTM), respectively.

Source: Ain Shams Engineering Journal

DOI: 10.1016/j.asej.2025.103678

Authors: R. Ithaya, R. Shobana, Ashis Kumar Mishra, J. Benitha Christinal

Institutions: Odisha University of Technology and Research, Presidency University

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: gold

Estimated APC paid: $1,200