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


  • ٍ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 |


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.


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


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X. Wu, Z. Jin, J. Zhou, and C. Duan, "Quantum walks-based classification model with resistance for cloud computing attacks," Expert Systems with Applications, vol. 232, Dec. 2023, Art. no. 120894.

Y. Liu, Z. Liu, S. Li, Y. Guo, Q. Liu, and G. Wang, "Cloud-Cluster: An uncertainty clustering algorithm based on cloud model," Knowledge-Based Systems, vol. 263, Mar. 2023, Art. no. 110261.

K. G. Maheswari, C. Siva, and G. Nalinipriya, "Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables deep recurrent neural network," Computer Communications, vol. 202, pp. 145–153, Mar. 2023.

D. Wang, "Internet of things sports information collection and sports action simulation based on cloud computing data platform," Preventive Medicine, vol. 173, Aug. 2023, Art. no. 107579.

X. Wu, "Big data classification of remote sensing image based on cloud computing and convolutional neural network," Soft Computing, Jan. 2022.

K. Singh and J. Malhotra, "IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification," Journal of Ambient Intelligence and Humanized Computing, Dec. 2019.

J. Wang, J. Yu, Y. Song, X. He, and Y. Song, "An efficient energy-aware and service quality improvement strategy applied in cloud computing," Cluster Computing, vol. 26, no. 6, pp. 4031–4049, Dec. 2023.

D. Thakur, J. K. Saini, and S. Srinivasan, "DeepThink IoT: The Strength of Deep Learning in Internet of Things," Artificial Intelligence Review, vol. 56, no. 12, pp. 14663–14730, Dec. 2023.

A. Alshammari, "A Novel Security Framework to Mitigate and Avoid Unexpected Security Threats in Saudi Arabia," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11445–11450, Aug. 2023.

A. Penuelas-Angulo, C. Feregrino-Uribe, and M. Morales-Sandoval, "Revocation in attribute-based encryption for fog-enabled internet of things: A systematic survey," Internet of Things, vol. 23, Oct. 2023, Art. no. 100827.

S. Krishnaveni and S. Prabakaran, "Ensemble approach for network threat detection and classification on cloud computing," Concurrency and Computation: Practice and Experience, vol. 33, no. 3, 2021, Art. no. e5272.

B. Alghamdi, L. E. Potter, and S. Drew, "Validation of Architectural Requirements for Tackling Cloud Computing Barriers: Cloud Provider Perspective," Procedia Computer Science, vol. 181, pp. 477–486, Jan. 2021.

M. Awad, "Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM)," in 3rd International Multidisciplinary Conference on Engineering Technology, Beirut, Lebanon, Dec. 2021, pp. 71–76.

S. Mian Qaisar and S. F. Hussain, "An effective arrhythmia classification via ECG signal subsampling and mutual information based subbands statistical features selection," Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3, pp. 1473–1487, Mar. 2023.

S. Husnain and R. Abdulkader, "Fractional Order Modeling and Control of an Articulated Robotic Arm," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12026–12032, Dec. 2023.

H. A. Almashhadani, X. Deng, O. R. Al-Hwaidi, S. T. Abdul-Samad, M. M. Ibrahm, and S. N. A. Latif, "Design of A new Algorithm by Using Standard Deviation Techniques in Multi Edge Computing with IoT Application," KSII Transactions on Internet and Information Systems, vol. 17, no. 4, pp. 1147–1161, Apr. 2023.

T. Zhao, L. Wu, D. Wu, J. Li, and Z. Cui, "Multi-factor Evolution for Large-scale Multi-objective Cloud Task Scheduling," KSII Transactions on Internet and Information Systems, vol. 17, no. 4, pp. 1100–1122, Apr. 2023.

A. Ghosh, D. De, and K. Majumder, "A Systematic Review of Log-Based Cloud Forensics," in Inventive Computation and Information Technologies, S. Smys, V. E. Balas, K. A. Kamel, and P. Lafata, Eds. New York, NY, USA: Springer, 2021, pp. 333–347.

V. S. Bai and T. Sudha, "A Systematic Literature Review on Cloud Forensics in Cloud Environment," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 4s, pp. 565–578, Feb. 2023.

G. Surange and P. Khatri, "IoT Forensics: A Review on Current Trends, Approaches and Foreseen Challenges," in 8th International Conference on Computing for Sustainable Global Development, New Delhi, India, Mar. 2021, pp. 909–913.

E. Saiti and T. Theoharis, "An application independent review of multimodal 3D registration methods," Computers & Graphics, vol. 91, pp. 153–178, Oct. 2020.

F. Alotaibi, A. Al-Dhaqm, and Y. D. Al-Otaibi, "A Conceptual Digital Forensic Investigation Model Applicable to the Drone Forensics Field," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11608–11615, Oct. 2023.

O. Ameerbakhsh, F. M. Ghabban, I. M. Alfadli, A. N. AbuAli, A. Al-Dhaqm, and M. A. Al-Khasawneh, "Digital Forensics Domain and Metamodeling Development Approaches," in 2nd International Conference on Smart Computing and Electronic Enterprise, Cameron Highlands, Malaysia, Jun. 2021, pp. 67–71.

K. Tange, M. De Donno, X. Fafoutis, and N. Dragoni, "A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2489–2520, 2020.

G. Adamo, C. Ghidini, and C. Di Francescomarino, "What is a process model composed of? A systematic literature review of meta-models in BPM," Software and Systems Modeling, vol. 20, no. 4, pp. 1215–1243, Dec. 2021.

A. Diab, R. Kashef, and A. Shaker, "Deep Learning for LiDAR Point Cloud Classification in Remote Sensing," Sensors, vol. 22, no. 20, Jan. 2022, Art. no. 7868.

H. Zhang et al., "Deep learning-based 3D point cloud classification: A systematic survey and outlook," Displays, vol. 79, Sep. 2023, Art. no. 102456.

H. Daghigh, D. D. Tannant, V. Daghigh, D. D. Lichti, and R. Lindenbergh, "A critical review of discontinuity plane extraction from 3D point cloud data of rock mass surfaces," Computers & Geosciences, vol. 169, Dec. 2022, Art. no. 105241.

H. Shukur et al., "A State of Art Survey for Concurrent Computation and Clustering of Parallel Computing for Distributed Systems," Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 148–154, Dec. 2020.

A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, "K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data," Information Sciences, vol. 622, pp. 178–210, Apr. 2023.

H. Yu and X. Hou, "Hierarchical clustering in astronomy," Astronomy and Computing, vol. 41, Oct. 2022, Art. no. 100662.

N. Tissir, S. El Kafhali, and N. Aboutabit, "Cybersecurity management in cloud computing: semantic literature review and conceptual framework proposal," Journal of Reliable Intelligent Environments, vol. 7, no. 2, pp. 69–84, Jun. 2021.

W. Wang and S. Yongchareon, "Security-as-a-service: a literature review," International Journal of Web Information Systems, vol. 16, no. 5, pp. 493–517, Jan. 2020.

A. Ometov, O. L. Molua, M. Komarov, and J. Nurmi, "A Survey of Security in Cloud, Edge, and Fog Computing," Sensors, vol. 22, no. 3, Jan. 2022, Art. no. 927.

G. Li, H. Dong, and C. Zhang, "Cloud databases: new techniques, challenges, and opportunities," Proceedings of the VLDB Endowment, vol. 15, no. 12, pp. 3758–3761, Dec. 2022.

M. Stoyanova, Y. Nikoloudakis, S. Panagiotakis, E. Pallis, and E. K. Markakis, "A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues," IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 1191–1221, 2020.

S. Shiju George and R. Suji Pramila, "A review of different techniques in cloud computing," Materials Today: Proceedings, vol. 46, pp. 8002–8008, Jan. 2021.

M. Rahimi, N. Jafari Navimipour, M. Hosseinzadeh, M. H. Moattar, and A. Darwesh, "Toward the efficient service selection approaches in cloud computing," Kybernetes, vol. 51, no. 4, pp. 1388–1412, Jan. 2021.

M. Masdari and H. Khezri, "Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review," Cluster Computing, vol. 23, no. 4, pp. 2629–2658, Dec. 2020.

R. Al-Mugerrn, A. Al-Dhaqm, and S. H. Othman, "A Metamodeling Approach for Structuring and Organizing Cloud Forensics Domain," in International Conference on Smart Computing and Application, Hail, Saudi Arabia, Feb. 2023, pp. 1–5.

S. Sureshkumar, N. Kirthiga, T. A. Kumar, P. N. Kumar, Y. P. Kumar Reddy, and R. S. Reddy, "Dual Access Control for Cloud-Based Data Storage and Sharing," in 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, Vellore, India, Dec. 2023, pp. 1–6.

A. Hakiri, A. Gokhale, S. Ben Yahia, and N. Mellouli, "A Comprehensive Survey on Digital Twin for Future Networks and Emerging Iot Industry." Rochester, NY, USA, Aug. 09, 2023.

A. Naghib, N. Jafari Navimipour, M. Hosseinzadeh, and A. Sharifi, "A comprehensive and systematic literature review on the big data management techniques in the internet of things," Wireless Networks, vol. 29, no. 3, pp. 1085–1144, Apr. 2023.

Y. Jing, X. Lu, and S. Gao, "3D face recognition: A comprehensive survey in 2022," Computational Visual Media, vol. 9, no. 4, pp. 657–685, Dec. 2023.

L. Li, R. Wang, and X. Zhang, "A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges," Mathematical Problems in Engineering, vol. 2021, Jul. 2021, Art. no. e9953910.


How to Cite

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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