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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
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    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.
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