International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Machine Learning Model for Denial of Service Network Intrusion Detection

Stephen Ngure GitongaInformation Technology Department, Masinde Muliro University of Science and Technology, Kakamega, KenyaOtanga Ananda DanielInformation Technology Department, Masinde Muliro University of Science and Technology, Kakamega, KenyaStephen Makau MutuaComputer Science Department, Meru University of Science and Technology, Meru, Kenya

Vol 3 No 7 (2019): Volume 3, Issue 7, July 2019 | Pages: 22-28

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 10-07-2019

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Abstract

Every organization strives to ensure its network is secured from all sorts of attack and is available to its intended users all the time. Network Intrusion Detection Systems (IDS) is one of the techniques used to detect and classify abnormal network access. Therefore, IDS should always be up and up to date with intrusion types and techniques. The most common network intrusion is denial of service (DOS). This study seeks to find out the best machine learning tools that can be used to detect a DOS attack. Using Knowledge Discovery and Data Mining (Knowledge Discovery in Databases) KDD dataset, three machine learning tools are evaluated to find out their performance. The findings show that MLP performs better compared to SVM and KNN.

Keywords

IDS, Neural Network, DOS, SVM, KNN


Citation of this Article

Stephen Ngure Gitonga, Otanga Ananda Daniel, Stephen Makau Mutua, “Machine Learning Model for Denial of Service Network Intrusion Detection” Published in International Research Journal of Innovations in Engineering and Technology (IRJIET), Volume 3, Issue 7, pp 22-28, July 2019.

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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