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DOI: 10.14569/IJACSA.2024.0151269
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Cyber Security Risk Assessment Framework for Cloud Customer and Service Provider

Author 1: N. Sujata Kumari
Author 2: Naresh Vurukonda

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 12, 2024.

  • Abstract and Keywords
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Abstract: The rapid development of cloud computing demands an effective cybersecurity framework for protecting the sensitive information of the infrastructure. Currently, many organizations depend on cloud services for their operation, increasing the risk of cybersecurity. Hence, an intelligent risk assessment mechanism is significant for detecting and mitigating the cybersecurity threats associated with cloud environments. Although various risk assessment methods were developed in the past, they lack the efficiency to handle the dynamic and evolving nature of threats. In this study, we proposed an innovative framework for cybersecurity risk assessment in cloud customers and service providers. Initially, the historical cloud customer and service provider database was collected and fed into the system. The collected dataset contains historical security risks, network traffic, system behavior, etc., and the accumulated dataset was pre-processed to improve the quality of the dataset. The data pre-processing steps not only ensure quality but also transform the dataset into appropriate format for subsequent analysis. Further, a risk assessment module was created using the combination of deep recurrent neural network with krill herd optimization (DRNN-KHO) algorithm. In this module, the DRNN was trained using the pre-processed database to learn the pattern and interconnection between normal and abnormal network traffic. Subsequently, the KHO refines the DRNN parameters in its training phase, increasing the efficiency of risk assessment. This integrated module ensures adaptability to the system, leading to accurate prediction of evolving security threats. Then, a secure data exchange protocol was created for secure transmission between cloud customer and service provider. This protocol is designed by integrating artificial bee colony optimization with the elliptic curve cryptography (ABC-ECC). Thus, this collaborative framework ensures security in the cloud customer and service providers.

Keywords: Deep recurrent neural network; krill herd optimization; artificial bee colony optimization; elliptic curve cryptography

N. Sujata Kumari and Naresh Vurukonda, “Cyber Security Risk Assessment Framework for Cloud Customer and Service Provider” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151269

@article{Kumari2024,
title = {Cyber Security Risk Assessment Framework for Cloud Customer and Service Provider},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151269},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151269},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {N. Sujata Kumari and Naresh Vurukonda}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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