IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2204-d1045998.html
   My bibliography  Save this article

Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment

Author

Listed:
  • Amit Sagu

    (Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India)

  • Nasib Singh Gill

    (Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India)

  • Preeti Gulia

    (Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, India)

  • Pradeep Kumar Singh

    (School of Technology Management & Engineering, Narsee Monjee Institute of Management Studies (NMIMS), Chandigarh 160014, India)

  • Wei-Chiang Hong

    (Department of Information Management, Asia Eastern University of Science and Technology, New Taipei 22046, Taiwan
    Department of Information Management, Yuan Ze University, Chungli 320315, Taiwan)

Abstract

Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.

Suggested Citation

  • Amit Sagu & Nasib Singh Gill & Preeti Gulia & Pradeep Kumar Singh & Wei-Chiang Hong, 2023. "Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2204-:d:1045998
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pooja Anand & Yashwant Singh & Arvind Selwal & Pradeep Kumar Singh & Raluca Andreea Felseghi & Maria Simona Raboaca, 2020. "IoVT: Internet of Vulnerable Things? Threat Architecture, Attack Surfaces, and Vulnerabilities in Internet of Things and Its Applications towards Smart Grids," Energies, MDPI, vol. 13(18), pages 1-23, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abrar Yaqoob & Rabia Musheer Aziz & Navneet Kumar Verma & Praveen Lalwani & Akshara Makrariya & Pavan Kumar, 2023. "A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification," Mathematics, MDPI, vol. 11(5), pages 1-32, February.
    2. Dominic Lightbody & Duc-Minh Ngo & Andriy Temko & Colin C. Murphy & Emanuel Popovici, 2023. "Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection," Future Internet, MDPI, vol. 15(5), pages 1-27, May.

    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. Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.

    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:gam:jsusta:v:15:y:2023:i:3:p:2204-:d:1045998. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.