Volume 11 , Issue 1 , PP: 22-29, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Manal M. Nasir 1 * , Salim M. Hebrisha 2
Doi: https://doi.org/10.54216/JCIM.110102
Cloud computing has become a popular paradigm for delivering computing resources and services over the internet. However, the adoption of cloud computing also brings new security challenges and risks, including data breaches, insider attacks, and unauthorized access. Therefore, it is critical to have a comprehensive information security management framework to address these challenges and ensure the security and privacy of cloud computing environments. This paper proposes a machine learning (ML) based information security management (ISM) framework for cloud computing environments that integrates best practices and standards from various domains, including cloud computing, information security, and risk management. The proposed framework includes residual recurrent network to effectively discriminate different patterns of cloud security attacks. The proposed framework emphasizes the importance of threat detection, security controls, and continuous monitoring and improvement. The framework is designed to be flexible and scalable, allowing organizations to tailor it to their specific needs and requirements.
Information Security , Information Management , Cloud Computing , Machine Learning
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