IDEAS home Printed from https://ideas.repec.org/a/eee/ijocip/v50y2025ics1874548225000307.html

AI-enhanced intrusion detection in smart renewable energy grids: A novel industry 4.0 cyber threat management approach

Author

Listed:
  • Islam, Umar
  • Ullah, Hanif
  • Khan, Naveed
  • Saleem, Kashif
  • Ahmad, Iftikhar

Abstract

The rapid adoption of Industry 4.0 technologies in renewable energy grids has significantly improved efficiency and scalability. However, this integration has also amplified cybersecurity risks, making conventional Intrusion Detection Systems (IDS) insufficient against evolving cyber threats. This study proposes a novel AI-enhanced Intrusion Detection System (IDS) tailored for smart renewable energy grids, leveraging a multi-stage detection framework that integrates both supervised and unsupervised learning techniques. The proposed IDS combines Random Forest for signature-based detection and Autoencoders for anomaly-based threat identification, enabling real-time detection of both known and zero-day cyber threats. A comprehensive evaluation using real-world cyberattack datasets demonstrates that the system achieves a detection accuracy of 97.8 %, significantly reducing false positives compared to traditional IDS solutions. This work not only enhances the security and resilience of smart grids but also offers a scalable and adaptable cybersecurity framework for Industry 4.0 applications. The findings contribute to the advancement of AI-driven security mechanisms, ensuring the reliability of critical energy infrastructure in the face of sophisticated cyber threats.

Suggested Citation

  • Islam, Umar & Ullah, Hanif & Khan, Naveed & Saleem, Kashif & Ahmad, Iftikhar, 2025. "AI-enhanced intrusion detection in smart renewable energy grids: A novel industry 4.0 cyber threat management approach," International Journal of Critical Infrastructure Protection, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:ijocip:v:50:y:2025:i:c:s1874548225000307
    DOI: 10.1016/j.ijcip.2025.100769
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1874548225000307
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijcip.2025.100769?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 720-732.
    2. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
    4. Yoldaş, Yeliz & Önen, Ahmet & Muyeen, S.M. & Vasilakos, Athanasios V. & Alan, İrfan, 2017. "Enhancing smart grid with microgrids: Challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 205-214.
    5. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Tianhao & Shan, Linke & Jiang, Meihui & Li, Fangning & Kong, Fannie & Du, Pengcheng & Zhu, Hongyu & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Zhang, Dongdong, 2025. "Multi-dimensional data processing and intelligent forecasting technologies for renewable energy generation," Applied Energy, Elsevier, vol. 398(C).
    2. Yeongenn Kwon & Taeyoung Kim & Keon Baek & Jinho Kim, 2020. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment," Energies, MDPI, vol. 13(11), pages 1-16, June.
    3. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    4. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.
    5. Jiang, Luanjuan & Chen, Xin & Li, Qianmu, 2025. "Evolution of smart grid cybersecurity: toward a systematic framework for collaborative and sustainable development," Utilities Policy, Elsevier, vol. 97(C).
    6. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    7. Nazir, Lubna & Sharifi, Ayyoob, 2024. "An analysis of barriers to the implementation of smart grid technology in Pakistan," Renewable Energy, Elsevier, vol. 220(C).
    8. Liu Wu & Min Liu & Ke Gong & Liudan Jiao & Xiaosen Huo & Yu Zhang & Hao Wang, 2025. "The Usage of Big Data in Electric Vehicle Charging: A Comprehensive Review," Energies, MDPI, vol. 18(19), pages 1-25, September.
    9. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    10. Peter, Nirma & Gupta, Pankaj & Goel, Nidhi, 2025. "Intelligent strategies for microgrid protection: A comprehensive review," Applied Energy, Elsevier, vol. 379(C).
    11. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    12. Jurasz, Jakub & Beluco, Alexandre & Canales, Fausto A., 2018. "The impact of complementarity on power supply reliability of small scale hybrid energy systems," Energy, Elsevier, vol. 161(C), pages 737-743.
    13. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    14. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    15. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    16. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
    17. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    18. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
    19. Chisom E. Ogbogu & Jesse Thornburg & Samuel O. Okozi, 2025. "Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review," Energies, MDPI, vol. 18(4), pages 1-26, February.
    20. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijocip:v:50:y:2025:i:c:s1874548225000307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-critical-infrastructure-protection .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.