An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments

Authors

  • ٍRafef Al-mugern Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia | Department of Computer Science, Shaqra University, Saudi Arabia
  • Siti Hajar Othman Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
  • Arafat Al-Dhaqm Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Malaysia
Volume: 14 | Issue: 1 | Pages: 13017-13025 | February 2024 | https://doi.org/10.48084/etasr.6609

Abstract

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts’ data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments.

Keywords:

digital forensics, data collection, cloud environment, cloud computing, data integrity

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How to Cite

[1]
Al-mugern ٍ., S. H. Othman, and A. Al-Dhaqm, “An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 13017–13025, Feb. 2024.

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