Open Access

ARTICLE

Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Muhammad Nadeem¹, Ali Arshad², Saman Riaz², SyedaWajiha Zahra¹, Muhammad Rashid², Shahab S. Band^3,*, Amir Mosavi^4,5,6
1 Department of Computer Science, Abasyn University, Islamabad, 44000, Pakistan
2 Department of Computer Science, National University of Technology, Islamabad, 44000, Pakistan
3 Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Yunlin, 64002, Taiwan
4 Institute of Information Society, University of Public Service, Budapest, 1083, Hungary
5 John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
6 Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Slovakia
* Corresponding Author: Shahab S. Band. Email:

Computers, Materials & Continua 2023, 74(2), 2641-2659. https://doi.org/10.32604/cmc.2023.028796

Received 18 February 2022; Accepted 25 May 2022; Issue published 31 October 2022

Abstract

Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be intercepted through two effective techniques: Cloudflare and K-nearest neighbors (KNN) classification. Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks. However, the KNN classifiers will determine which area the spammer belongs to. At the end of the article, various prevention techniques for securing cloud servers will be discussed, a comparison will be made with different papers, a conclusion will be drawn based on different results.

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Keywords

Intrusion prevention system; spamming; KNN classification; spam; cyber security; botnet

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