Detecting Cybersecurity Threats in Digital Energy Systems Using Deep learning for Imbalanced Datasets

Abstract

Energy management systems are experiencing significant transformations due to the adoption of innovative business models and advanced digital technologies. This study aims to investigate the intersection of artificial intelligence and cybersecurity within energy infrastructures, specifically focusing on developing a comprehensive methodology that effectively detects security threats in digitalized systems. The research evaluates existing energy policies and regulations while emphasizing the critical role of deep learning algorithms in enhancing cybersecurity through advanced threat detection, predictive analytics, automated responses, and continuous learning capabilities. A significant aspect of this study is the effective handling of imbalanced datasets, which is essential for optimizing deep learning performance in cybersecurity applications. Furthermore, the paper presents a comparative analysis of network intrusion detection systems and proposes a feature selection methodology by a novel feature reduction methodology designed to enhance deep learning capabilities for addressing specific challenges in imbalanced datasets of critical energy infrastructure. The expected results include insights into how artificial intelligence-driven methodologies can effectively mitigate cybersecurity threats in energy systems through a robust hybrid deep learning framework that addresses imbalanced datasets via advanced feature reduction techniques. Ultimately, this research contributes to enhancing both the immediate security of energy infrastructures and their long-term resilience against evolving cyber threats. By clarifying the contributions of deep learning methods to the literature on supervisory control and data acquisition system security, this study aims to bridge existing gaps and provide actionable insights for practitioners and policymakers in the energy sector and integrates regulatory frameworks (EU AI Act, NIST CSF 2.0, ISO/IEC standards) with a hybrid deep learning model addressing spatial, temporal, and structural intrusion patterns in SCADA systems using imbalanced data and a novel feature selection methodology within artificial intelligence.