Network Attack Detection for Business Safety

Fadia Abduljabbar Saeed, Ghalia Nassreddine and Joumana Younis ()
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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

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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.

Keywords: Machine learning; Networks attack; Network security; Data privacy; Deep Learning; Support vector machine; Artificial Neural network (search for similar items in EconPapers)
Date: 2024-03-15
Note: View the original document on HAL open archive server: https://hal.science/hal-04579363
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Published in NTU Journal of Engineering and Technology, 2024, 3 (1), ⟨10.56286/ntujet.v3i1.535⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04579363

DOI: 10.56286/ntujet.v3i1.535

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