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Network Attack Detection for Business Safety

Author

Listed:
  • Fadia Abduljabbar Saeed

    (Northern Technical University)

  • Ghalia Nassreddine

    (RHU - Rafik Hariri University)

  • Joumana Younis

    (DICEN-IDF - Dispositifs d'Information et de Communication à l'Ère du Numérique - Paris Île-de-France - UPN - Université Paris Nanterre - CNAM - Conservatoire National des Arts et Métiers [CNAM] - Université Gustave Eiffel)

Abstract

In the technology age, the use of networks has hugely increased. this led to an increment in the number of attackers. A network attack is an try to achieve unauthorized access to personnel of an organization's network, steal data or perform other malicious activity. Machine Learning is a subset of artificial Intelligence techniques that teaches machines to learn from historical information. In this paper, a machine learning-based approach was developed to detect network attacks. Two Machine learning models were used: Support vector machine and Artificial neural network. In this approach, a feature selection step based on the p-value is executed first to reduce the size of the dataset. After that, training and testing steps were performed. The proposed approach was tested on a real dataset collected from Kaggle. Confusion matrix, recall, precision, and f1 score were used to test the performance of the used ML techniques. The result shows the efficiency of this approach.

Suggested Citation

  • Fadia Abduljabbar Saeed & Ghalia Nassreddine & Joumana Younis, 2024. "Network Attack Detection for Business Safety," Post-Print hal-04579363, HAL.
    • Handle: RePEc:hal:journl:hal-04579363
    DOI: 10.56286/ntujet.v3i1.535
    Note: View the original document on HAL open archive server: https://hal.science/hal-04579363
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    References listed on IDEAS

    as
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    2. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
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