research-article

Network intrusion detection using Machine Learning approach

Authors:

Zhour Rachidi,

Khalid Chougdali,

Abdellatif Kobbane,

Jalel Ben-OthmanAuthors Info & Claims

ICICM '22: Proceedings of the 12th International Conference on Information Communication and Management

Pages 13 - 17

https://doi.org/10.1145/3551690.3551693

Published: 14 September 2022 Publication History

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Abstract

Abstract. Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Naïve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization.

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Huaraca Nuñez JCervantes Ccasa AAquino Cruz M(2024)Técnicas de machine learning para la detección de intrusos en redes: Una revisión sistemática de la literaturaMachine learning techniques for detecting intrusions in networks: A systematic review of the literatureMicaela Revista de Investigación - UNAMBA10.57166/micaela.v5.n2.2024.1515:2(17-24)Online publication date: 29-Oct-2024
https://doi.org/10.57166/micaela.v5.n2.2024.151
Fernando CThomas RE A MLapina M(2024)A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques2024 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC460750.2024.10649035(1-9)Online publication date: 15-Mar-2024
https://doi.org/10.1109/InC460750.2024.10649035

Index Terms

Network intrusion detection using Machine Learning approach
1. Security and privacy

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Published In

ICICM '22: Proceedings of the 12th International Conference on Information Communication and Management

July 2022

105 pages

ISBN:9781450396493

DOI:10.1145/3551690

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 September 2022

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ICICM 2022: 2022 The 12th International Conference on Information Communication and Management

July 13 - 15, 2022

London, United Kingdom

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Huaraca Nuñez JCervantes Ccasa AAquino Cruz M(2024)Técnicas de machine learning para la detección de intrusos en redes: Una revisión sistemática de la literaturaMachine learning techniques for detecting intrusions in networks: A systematic review of the literatureMicaela Revista de Investigación - UNAMBA10.57166/micaela.v5.n2.2024.1515:2(17-24)Online publication date: 29-Oct-2024
https://doi.org/10.57166/micaela.v5.n2.2024.151
Fernando CThomas RE A MLapina M(2024)A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques2024 IEEE International Conference on Contemporary Computing and Communications (InC4)10.1109/InC460750.2024.10649035(1-9)Online publication date: 15-Mar-2024
https://doi.org/10.1109/InC460750.2024.10649035

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An auto-learning approach for network intrusion detection

Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

An effective combining classifier approach using tree algorithms for network intrusion detection

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