IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i22p4607-d1277969.html
   My bibliography  Save this article

Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model

Author

Listed:
  • Rayed AlGhamdi

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While the utilization of the internet amongst consumers is increasing on a daily basis, the significance of security and privacy preservation of system alerts, due to malicious actions, is also increasing. IDS is a widely executed system that protects computer networks from attacks. For the identification of unknown attacks and anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, in real-world applications, the classification performances of these approaches are fluctuant with distinct databases. The major reason for this drawback is the presence of some ineffective or redundant features. So, the current study proposes the Network Intrusion Detection System using a Lion Optimization Feature Selection with a Deep Learning (NIDS-LOFSDL) approach to remedy the aforementioned issue. The NIDS-LOFSDL technique follows the concept of FS with a hyperparameter-tuned DL model for the recognition of intrusions. For the purpose of FS, the NIDS-LOFSDL method uses the LOFS technique, which helps in improving the classification results. Furthermore, the attention-based bi-directional long short-term memory (ABiLSTM) system is applied for intrusion detection. In order to enhance the intrusion detection performance of the ABiLSTM algorithm, the gorilla troops optimizer (GTO) is deployed so as to perform hyperparameter tuning. Since trial-and-error manual hyperparameter tuning is a tedious process, the GTO-based hyperparameter tuning process is performed, which demonstrates the novelty of the work. In order to validate the enhanced solution of the NIDS-LOFSDL system in terms of intrusion detection, a comprehensive range of experiments was performed. The simulation values confirm the promising results of the NIDS-LOFSDL system compared to existing DL methodologies, with a maximum accuracy of 96.88% and 96.92% on UNSW-NB15 and AWID datasets, respectively.

Suggested Citation

  • Rayed AlGhamdi, 2023. "Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4607-:d:1277969
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/22/4607/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/22/4607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Iyad Katib & Mahmoud Ragab, 2023. "Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment," Mathematics, MDPI, vol. 11(8), pages 1-16, April.
    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. Fatmah Y. Assiri & Mahmoud Ragab, 2023. "Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    2. Walid I. Khedr & Ameer E. Gouda & Ehab R. Mohamed, 2023. "P4-HLDMC: A Novel Framework for DDoS and ARP Attack Detection and Mitigation in SD-IoT Networks Using Machine Learning, Stateful P4, and Distributed Multi-Controller Architecture," Mathematics, MDPI, vol. 11(16), pages 1-36, August.

    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:jmathe:v:11:y:2023:i:22:p:4607-:d:1277969. 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.