An Intelligent Cybersecurity Model for Smart Grid Protection Using Deep Learning

Abstract

The modernization of electrical power systems through smart grid technology has significantly improved energy efficiency, real-time monitoring, automation, and demand-response capabilities. However, this transformation has also introduced serious cybersecurity vulnerabilities due to the integration of Internet of Things (IoT) devices, cloud computing platforms, Supervisory Control and Data Acquisition (SCADA) systems, and advanced communication networks. These interconnected components expose smart grids to a wide range of cyber threats, including false data injection attacks, denial-of-service attacks, ransomware infiltration, malware propagation, and unauthorized access to critical infrastructure. This study presents an intelligent cybersecurity model for smart grid protection using deep learning techniques. The proposed framework integrates multiple neural network architectures—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Autoencoders—to provide a comprehensive, adaptive, and real-time cyber defense mechanism. The system is designed to perform intelligent feature extraction, behavioral pattern recognition, anomaly detection, predictive threat analysis, and automated attack response. The model was evaluated using benchmark datasets relevant to power system security and network intrusion detection. Experimental results demonstrate that the proposed approach achieves high detection accuracy, improved precision and recall, reduced false-positive rates, and significantly faster response times compared to traditional machine learning-based security models. The findings confirm that deep learning-based cybersecurity systems offer a highly effective and scalable solution for protecting modern smart grid infrastructures.