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ARTICLE
Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach
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:
Journal of Cyber Security 2024, 6, 155-177. https://doi.org/10.32604/jcs.2024.059265
Received 01 October 2024; Accepted 20 November 2024; Issue published 09 January 2025
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.Keywords
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