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Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach

by Theophile Fozin Fonzin1,2, Halilou Claude Bobo Hamadjida2, Aurelle Tchagna Kouanou2,3,*, Valery Monthe4, Anicet Brice Mezatio5, Michael Sone Ekonde6

1 Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, Buea, P.O. Box 63, Cameroon
2 Department of Training, Research, Development and Innovation, InchTech’s Solutions, Yaounde, P.O. Box 30109, Cameroon
3 Department of Computer Engineering, College of Technology, University of Buea, Buea, P.O. Box 63, Cameroon
4 Department of Computer Science, Faculty of Sciences, University of Yaoundé 1, Yaoundé, P.O. Box 812, Cameroon
5 South Polytech, Institut Universitaire des Grandes Ecoles des Tropiques (IUGET), Douala, P.O. Box 25080, Cameroon
6 Department of Electrical and Electronic Engineering, College of Technology, University of Buea, Buea, P.O. Box 63, Cameroon

* Corresponding Author: Aurelle Tchagna Kouanou. Email: email

Journal of Cyber Security 2024, 6, 155-177. https://doi.org/10.32604/jcs.2024.059265

Abstract

Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design in a cloud computing environment, Azure environment (Microsoft) and its concept of Virtual Private Cloud (VPC) were used. Then, an IPS design was deployed with a ruleset from a mined dataset (via K-means clustering) and processed. Finally, the correlation between the traffic analyzed (virtual network traffic in real-time, logs) and the filtering rules or ruleset of this IPS made it possible to obtain and discuss on a precision rate of around 0.9 in True Positive Rate (TPR) in the prevention Cross-Site Scripting (XSS) attacks targeting the cloud, for a latent time of approximately 6.4 ms. Subsequently, it is important to think about extending the detection capabilities, attack complexity, and high traffic consideration of this IPS.

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APA Style
Fozin Fonzin, T., Hamadjida, H.C.B., Kouanou, A.T., Monthe, V., Mezatio, A.B. et al. (2024). Enhancing private cloud based intrusion prevention and detection system: an unsupervised machine learning approach. Journal of Cyber Security, 6(1), 155–177. https://doi.org/10.32604/jcs.2024.059265
Vancouver Style
Fozin Fonzin T, Hamadjida HCB, Kouanou AT, Monthe V, Mezatio AB, Ekonde MS. Enhancing private cloud based intrusion prevention and detection system: an unsupervised machine learning approach. J Cyber Secur. 2024;6(1):155–177. https://doi.org/10.32604/jcs.2024.059265
IEEE Style
T. Fozin Fonzin, H. C. B. Hamadjida, A. T. Kouanou, V. Monthe, A. B. Mezatio, and M. S. Ekonde, “Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach,” J. Cyber Secur., vol. 6, no. 1, pp. 155–177, 2024. https://doi.org/10.32604/jcs.2024.059265



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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