IDEAS home Printed from https://ideas.repec.org/a/igg/jisp00/v17y2023i1p1-17.html
   My bibliography  Save this article

i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security

Author

Listed:
  • Sabrine Ennaji

    (Sidi Mohamed Ben Abdellah University, Morocco)

  • Nabil El Akkad

    (National School of Applied Sciences, Morocco)

  • Khalid Haddouch

    (National School of Applied Sciences, Morocco)

Abstract

The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality of data, overfitting, imbalanced problems, etc. may greatly affect the performance of an intelligent intrusion detection system (IDS). To tackle these issues, this paper proposes a novel machine learning-based IDS called i-2NIDS. The novelty of this approach lies in the application of the nested cross-validation method, which necessitates using two loops: the outer loop is for hyper-parameter selection that costs least error during the run of a small amount of training set and the inner loop for the error estimation in the test set. The experiments showed significant improvements within NSL-KDD dataset with a test accuracy rate of 99.97%, 99.79%, 99.72%, 99.96%, and 99.98% in detecting normal activities, DDoS/DoS, Probing, R2L and U2R attacks, respectively. The obtained results approve the efficiency and superiority of the approach over other recent existing experiments.

Suggested Citation

  • Sabrine Ennaji & Nabil El Akkad & Khalid Haddouch, 2023. "i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 17(1), pages 1-17, January.
    • Handle: RePEc:igg:jisp00:v:17:y:2023:i:1:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISP.317113
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alok Kumar Shukla & Pradeep Singh, 2019. "Building an Effective Approach toward Intrusion Detection Using Ensemble Feature Selection," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 13(3), pages 31-47, July.
    2. Chikh Ramdane & Salim Chikhi, 2014. "A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 8(4), pages 1-25, October.
    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. Yuan Tian & Wendong Wang & Jingyuan He, 2024. "An IIoT Temporal Data Anomaly Detection Method Combining Transformer and Adversarial Training," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 18(1), pages 1-28, January.

    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. Kuldeep Singh & Lakhwinder Kaur & Raman Maini, 2022. "A survey of intrusion detection techniques based on negative selection algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 175-185, March.

    More about this item

    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:igg:jisp00:v:17:y:2023:i:1:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.