Digital Technologies Research and Applications

Review

Exploring Machine Learning Algorithms to Enhance Cloud Computing Security

Downloads

https://doi.org/10.54963/dtra.v4i2.1272
Ţălu, M. (2025). Exploring Machine Learning Algorithms to Enhance Cloud Computing Security. Digital Technologies Research and Applications, 4(2), 33–47. https://doi.org/10.54963/dtra.v4i2.1272
Volume 4 Issue 2: September 2025
Copyright (c) 2025 Mircea Ţălu
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Digital Technologies Research and Applications (DTRA)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by DTRA agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source are properly cited, anyone may copy, redistribute, reuse, and transform the content.

Authors

  • Mircea Ţălu

    Faculty of Automation and Computer Science, Technical University of Cluj‑Napoca, Cluj‑Napoca, Cluj 400027, Romania
    SC ACCESA IT SYSTEMS SRL, Cluj‑Napoca, Cluj 400158, Romania

Received: 26 May 2025; Revised: 6 June 2025; Accepted: 10 June 2025; Published: 3 July 2025

The increasing adoption of cloud computing (CC) has introduced significant security and privacy concerns, demanding intelligent and adaptive solutions. This review explores the application of machine learning (ML) algorithms—both supervised and unsupervised—in addressing these challenges within cloud environments. A total of 87 peer‑reviewed studies published between 2014 and 2025 were analyzed to assess the effectiveness of various ML techniques. Supervised Machine Learning (SML) algorithms such as Artificial Neural Networks (ANNs), Support Vector Machines (SVM), K‑Nearest Neighbors (K‑NN), Naive Bayes, and C4.5 Decision Trees are examined for their effectiveness in intrusion detection, anomaly classification, and threat mitigation. Concurrently, Unsupervised Machine Learning (UML) algorithms, including Unsupervised Neural Networks (UNNs), K‑Means clustering, and Singular Value Decomposition (SVD), are analyzed for their capacity to detect unknown threats and extract latent patterns from unlabeled data. Key trends reveal a growing preference for hybrid models, the superior accuracy of deep learning in anomaly detection, and the emerging use of context‑aware frameworks. The review shows a comparative analysis of these approaches, highlighting their advantages, limitations, and application scenarios in cloud security. Future research directions are proposed, emphasizing hybrid learning models, enhanced datasets, and context‑aware security frameworks. The findings underscore the transformative potential of ML in fortifying cloud infrastructures against evolving cyber threats.

Keywords:

Cloud Computing Cloud Security Machine Learning Security Threats Storage‑Based Attacks VM‑ Based Attacks Machine Learning Algorithms

References

  1. Antonopoulos, N.; Gillam, L. Cloud Computing: Principles, Systems and Applications, 1st ed.; Springer: Cham, Switzerland, 2017.
  2. Wegener, A. Cloud Computing: Systems and Technologies, 1st ed.; Clanrye International: New York, NY, USA, 2019; pp. 210–220.
  3. Comer, D. The Cloud Computing Book, 1st ed.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2021; pp. 5–12.
  4. Khalil, I.M.; Khreishah, A.; Azeem, M. Cloud computing security: A survey. Computers 2014, 3, 1–35. DOI: https://doi.org/10.3390/computers3010001
  5. Butt, U.A.; Mehmood, M.; Shah, S.B.H.; et al. A review of machine learning algorithms for cloud computing security. Electronics 2020, 9, 1379. DOI: https://doi.org/10.3390/electronics9091379
  6. Almutairi, M.; Sheldon, F.T. IoT–cloud integration security: A survey of challenges, solutions, and directions. Electronics 2025, 14, 1394, 1–28. DOI: https://doi.org/10.3390/electronics14071394
  7. Vacca, J.R. Cloud Computing Security: Foundations and Challenges, 2nd ed.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2020; pp. 64–70. DOI: https://doi.org/10.1201/9780429055126
  8. Harkut, D.G. Cloud Computing Security: Concepts and Practice; IntechOpen: London, UK, 2020; pp. 6–10. DOI: https://doi.org/10.5772/intechopen.83221
  9. Achari, A. Cybersecurity in Cloud Computing; Educohack Press: Delhi, India, 2025; pp. 18–22.
  10. Abdulsalam, Y.S.; Hedabou, M. Security and privacy in cloud computing: Technical review. Future Internet 2022, 14, 11. DOI: https://doi.org/10.3390/fi14010011
  11. Chauhan, M.; Shiaeles, S. An analysis of cloud security frameworks, problems and proposed solutions. Network 2023, 3, 422–450. DOI: https://doi.org/10.3390/network3030018
  12. Khan, M.A.; Khan, S.M.; Subramaniam, S.K. A systematic literature review on security issues in cloud computing using edge computing and blockchain: Threat, mitigation, and future trends. Malays. J. Comput. Sci. 2023, 36, 347–367. DOI: https://doi.org/10.22452/mjcs.vol36no4.1
  13. Ahmad, W.; Rasool, A.; Javed, A.R.; et al. Cybersecurity in IoT-based cloud computing: A comprehensive survey. Electronics 2022, 11, 16. DOI: https://doi.org/10.3390/electronics11010016
  14. Țălu, M. A review of vulnerability discovery in WebAssembly binaries: Insights from static, dynamic, and hybrid analysis. Acta Tech. Corviniensis Bull. Eng. 2024, 17, 13–22.
  15. Țălu, M. A review of advanced techniques for data protection in WebAssembly. Ann. Fac. Eng. Hunedoara Int. J. Eng. 2024, 22, 131–136.
  16. Țălu, M. A comparative study of WebAssembly runtimes: Performance metrics, integration challenges, application domains, and security features. Arch. Adv. Eng. Sci. 2025, 1–13. DOI: https://doi.org/10.47852/bonviewAAES52024965
  17. Țălu, M. Security and privacy in the IIoT: Threats, possible security countermeasures, and future challenges. Comput. AI Connect 2025, 2, 1–10. DOI: https://doi.org/10.69709/CAIC.2025.139199
  18. Țălu, M. Cyberattacks and cybersecurity: Concepts, current challenges, and future research directions. Digit. Technol. Res. Appl. 2025, 4, 44–60. DOI: https://doi.org/10.54963/dtra.v4i1.919
  19. Parast, F.K.; Sindhav, C.; Nikam, S.; et al. Cloud computing security: A survey of service-based models. Comput. Secur. 2022, 114, 102580. DOI: https://doi.org/10.1016/j.cose.2021.102580
  20. Subramanian, N.; Jeyaraj, A. Recent security challenges in cloud computing. Comput. Electr. Eng. 2018, 71, 28–42. DOI: https://doi.org/10.1016/j.compeleceng.2018.06.006
  21. Pavithra, B.; Vinola, C.; Mishra, N.; et al. Cloud security analysis using machine learning algorithms. In Proceedings of the Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 23–25 August 2023; pp. 704–708. DOI: https://doi.org/10.1109/ICAISS58487.2023.10250594
  22. Rainio, O.; Teuho, J.; Klén, R.B. Evaluation metrics and statistical tests for machine learning. Sci. Rep. 2024, 14, 6086. DOI: https://doi.org/10.1038/s41598-024-56706-x
  23. Nassif, A.B.; Talib, M.A.; Nasir, Q.; et al. Machine learning for cloud security: A systematic review. IEEE Access 2021, 9, 20717–20735. DOI: https://doi.org/10.1109/ACCESS.2021.3054129