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Open Access

Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques

Hanaa Attou1, Azidine Guezzaz1( ), Said Benkirane1, Mourade Azrour2, Yousef Farhaoui2
Technology Higher School Essaouira, Cadi Ayyad University, Marrakech 44000, Morocco.
STI Laboratory, the IDMS team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 25003, Morocco.
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Abstract

Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.

Keywords

cloud securityanomaly detectionfeatures engineeringrandom forest

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Big Data Mining and Analytics
Volume 6 Issue 3,
September 2023
Pages 311-320
DOI: 10.26599/BDMA.2022.9020038
Cite this article:
Attou H, Guezzaz A, Benkirane S, et al. Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques. Big Data Mining and Analytics, 2023, 6(3): 311-320. https://doi.org/10.26599/BDMA.2022.9020038

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Received: 07 September 2022
Revised: 27 September 2022
Accepted: 12 October 2022
Published: 07 April 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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