Vol.35, No.1, 2023, pp.79-95, doi:10.32604/iasc.2023.024291
Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm
  • R. S. Aashmi1, T. Jaya2,*
1 Department of Computer Science and Engineering, CSI Institute of Technology, Thovalai, India
2 Department of Electronic Communications and Engineering, CSI Institute of Technology, Thovalai, India
* Corresponding Author: T. Jaya. Email:
Received 12 October 2021; Accepted 04 January 2022; Issue published 06 June 2022

Cloud computing is the technology that is currently used to provide users with infrastructure, platform, and software services effectively. Under this system, Platform as a Service (PaaS) offers a medium headed for a web development platform that uniformly distributes the requests and resources. Hackers using Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks abruptly interrupt these requests. Even though several existing methods like signature-based, statistical anomaly-based, and stateful protocol analysis are available, they are not sufficient enough to get rid of Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks and hence there is a great need for a definite algorithm. Concerning this issue, we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis, Spearman coefficient, and mitigation technique. It can easily differentiate common traffic and attack traffic. Not only that, it greatly helps the network to distribute the resources only for authenticated requests. The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99% for the numerous DoS attack as well as DDoS attacks.

Hybrid algorithm (HA); distributed denial of service (DDoS); denial of service (DoS); platform as a service (PaaS); infrastructure as a service (IaaS); software as a service (SaaS)
Cite This Article
R. S. Aashmi and T. Jaya, "Detecting and preventing of attacks in cloud computing using hybrid algorithm," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 79–95, 2023.
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