Security on Cloud Resource Consumption of Malicious Attacks – A Systematic Review
Main Article Content
Keywords
MCBC Cloud Attack, Machine Learning, Cyber Security, Clouds Security, Online Consumption
Abstract
An ever-increasing number of buyers are moving to the public cloud and facilitating their applications on cloud cost-effectively to benefit from enhanced high accessibility and versatility. Malicious Cloud Bandwidth Consumption (MCBC) is one type of threat where attackers consume the cloud bandwidth slowly and continuously for an extended time, causing a financial burden to the cloud consumer. This research emphasises securing cloud web resource consumption by detailing MCBC attacks and proposes a methodology for building classifiers using Machine Learning (ML) to detect malicious requests accurately. Then, at that point, we evaluate the performance of each method based on its features, advantages, and limitations. Additionally, we also outline future research directions for developing improved cloud computing models.
References
Bobba, J. (2024). Securing financial data in cloud environments: AI and IaaS reliability verification techniques. International Journal of Applied Science Engineering and Management, 18(3), 538–552. https://doi.org/10.5281/zenodo.13994655
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
Carrera, G. (2022). Building a comprehensive cloud security audit program. EDPACS, 66(1), 15–18. https://doi.org/10.1080/07366981.2021.2004689
Deepa, P. L., Kumar, S. V., & Karthik, S. (2012). Searching techniques in encrypted cloud data. International Journal of Advanced Research in Computer Engineering & Technology, 1(8).
Deepa, P. L., Kumar, S. V., & Karthik, S. (2013). Improve search efficiency of encrypted cloud data. International Journal of Computer Science and Engineering, 1(1). Available at https://citeseerx.ist.psu.edu/document?doi=69e53c1af3433f0b846d04bc0e6859e9454f677e
Fernandes, D., Soares, L., Gomes, J., Freire, M., & Inácio, P. (2014). Security issues in cloud environments: A survey. International Journal of Information Security, 13(2), 113–170. https://doi.org/10.1007/s10207-013-0208-7
Gazdar, A., Hidri, L., Ben Youssef, B., & Kefi, M. (2021). Minimizing the in-cloud bandwidth for on-demand reactive and proactive streaming applications. Applied Sciences, 11(23), 11267. https://doi.org/10.3390/app112311267
Gupta, A., & Namasudra, S. (2022). A novel technique for accelerating live migration in cloud computing. Automated Software Engineering, 29(1), 1–21. https://doi.org/10.1007/s10515-022-00332-2
Karn, R. R., Kudva, P., & Elfadel, I. A. (2019). Dynamic autoselection and autotuning of machine learning models for cloud network analytics. IEEE Transactions on Parallel and Distributed Systems, 30(5), 1052–1064. https://doi.org/10.1109/TPDS.2018.2876844
Khalid, M. I. I., & Zolkipli, M. F. (2022). Review on cloud security and challenges in higher education. Malaysian Journal of Applied Sciences, 7(1), 1–9. https://doi.org/10.37231/myjas.2022.7.1.284
Manoharan, J. S. (2021). A novel user layer cloud security model based on chaotic Arnold transformation using fingerprint biometric traits. Journal of Innovative Image Processing, 3(1), 36–51. https://doi.org/10.36548/jiip.2021.1.004
Mohammadi, B., & Navimipour, N. J. (2022). A fuzzy logic-based method for replica placement in the peer-to-peer cloud using an optimization algorithm. Wireless Personal Communications, 122(2), 981–1005. https://doi.org/10.1007/s11277-021-08936-9
Ometov, A., Molua, O. L., Komarov, M., & Nurmi, J. (2022). A survey of security in cloud, edge, and fog computing. Sensors, 22(3), 927. https://doi.org/10.3390/s22030927
Srinivasan, K., Chauhan, G. S., Jadon, R., Budda, R., Gollapalli, V. S. T., & Prema, R. (2025). Enhancing cloud security through deep learning: Implementing LSTM-GRU based malware detection. International Journal of Engineering, Management and Humanities, 6(2), 58–63. Available at https://ijemh.com/issue_dcp/Enhancing%20Cloud%20Security%20Through%20Deep%20Learning%20%20Implementing%20LSTM%20GRU%20Based%20Malware%20Detection.pdf
Sun, N., Zhang, J., Rimba, P., Gao, S., Zhan, L. Y., & Xiang, Y. (2018). Data-driven cybersecurity incident prediction: A survey. IEEE Communications Surveys & Tutorials, 21(2), 1744–1772. https://doi.org/10.1109/COMST.2018.2885561
Zhang, Y. (2022). Research on network security intrusion identification and defense against malicious network damage in a cloud environment. International Journal of Network Security, 24(3), 551–556. https://doi.org/10.6633/IJNS.20220524(3).18
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Telecommunications Association Inc.
R Senthilkumar, Computer Science and Engineering, Alva’s Institute of Engineering and Technology, Karnataka, India
Senior Assistant Professor, Computer Science and Engineering, Alva’s Institute of Engineering and Technology, Karnataka, India
How to Cite
