 Cybersecurity Challenges in PV-Hydrogen Transport Networks: Leveraging Recursive Neural Networks for Resilient OperationLei Yang (),
Saddam Aziz and
Zhenyang Yu
Energies, 2025, vol. 18, issue 9, 1-20 Abstract: In the rapidly evolving landscape of transportation technologies, hydrogen vehicle networks integrated with photovoltaic (PV) systems represent a significant advancement toward sustainable mobility. However, the integration of such technologies also introduces complex cybersecurity challenges that must be meticulously managed to ensure operational integrity and system resilience. This paper explores the intricate dynamics of cybersecurity in PV-powered hydrogen vehicle networks, focusing on the real-time challenges posed by cyber threats such as False Data Injection Attacks (FDIAs) and their impact on network operations. Our research utilizes a novel hierarchical robust optimization model enhanced by Recursive Neural Networks (RNNs) to improve detection rates and response times to cyber incidents across various severity levels. The initial findings reveal that as the severity of incidents escalates from level 1 to 10, the response time significantly increases from an average of 7 min for low-severity incidents to over 20 min for high-severity scenarios, demonstrating the escalating complexity and resource demands of more severe incidents. Additionally, the study introduces an in-depth examination of the detection dynamics, illustrating that while detection rates generally decrease as incident frequency increases—due to system overload—the employment of advanced RNNs effectively mitigates this trend, sustaining high detection rates of up to 95% even under high-frequency scenarios. Furthermore, we analyze the cybersecurity risks specifically associated with the intermittency of PV-based hydrogen production, demonstrating how fluctuations in solar energy availability can create vulnerabilities that cyberattackers may exploit. We also explore the relationship between incident frequency, detection sensitivity, and the resulting false positive rates, revealing that the optimal adjustment of detection thresholds can reduce false positives by as much as 30%, even under peak load conditions. This paper not only provides a detailed empirical analysis of the cybersecurity landscape in PV-integrated hydrogen vehicle networks but also offers strategic insights into the deployment of AI-enhanced cybersecurity frameworks. The findings underscore the critical need for scalable, responsive cybersecurity solutions that can adapt to the dynamic threat environment of modern transport infrastructures, ensuring the sustainability and safety of solar-powered hydrogen mobility solutions. Keywords: cybersecurity in hydrogen networks; recursive neural networks (RNNs); false data injection attacks (FDIAs); hierarchical robust optimization; detection sensitivity; false positives; AI-enhanced cyber defense; solar systems (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:9:p:2262-:d:1645360 Access Statistics for this article Energies is currently edited by Ms. Agatha Cao More articles in Energies from MDPI |
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