Cyberattack Prevention and Detection in Smart Power Systems Using Deep Learning
Year: 2025
Type: article
Abstract: Cyber security in power systems is of paramount importance due to the critical nature of these infrastructures. With the increasing digitization of power systems, ensuring cyber security has become imperative to safeguard critical infrastructure. This paper investigates the utilization of meta-heuristic and deep learning algorithms to bolster cyber security in power systems. Traditional supervised machine learning algorithms, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Support Vector Machines (SVMs), are benchmarked against the proposed algorithm to assess their effectiveness. The proposed algorithm optimizes the hyper parameters and architectures of deep learning models, thereby improving their performance in detecting cyber threats. Cyber-attacks on power systems can have severe consequences, ranging from service disruptions to cascading failures with widespread societal impacts. This research paper investigates the integration of meta-heuristic and deep learning algorithms to enhance cyber security in power systems. Meta-heuristic algorithms offer efficient optimization solutions, while deep learning techniques excel in pattern recognition and anomaly detection. By combining these approaches, a comprehensive framework is proposed for threat detection and mitigation. The paper reviews existing literature, presents methodologies, and discusses potential benefits and challenges. Case studies and experimental results demonstrate the efficacy of the integrated approach in enhancing cyber security in power systems. This research contributes to the advancement of robust and adaptive cyber security measures for critical infrastructure protection.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083584634
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