Abstract
The emergence of cloud computing has transformed the way businesses manage and distribute their digital resources. However, it has also introduced new vulnerabilities and risks, with cyber-attacks exploiting the cloud’s elasticity and scalability features. This paper explores the application of Machine Learning (ML) and Deep Learning (DL) techniques to detect and mitigate Economic Denial of Sustainability (EDoS) attacks, focusing on simulations conducted using CloudSim. Our findings demonstrate the efficacy of these techniques in enhancing cloud security. The simulation results indicate that the proposed system can detect EDoS attacks with an accuracy of 94% and reduce false positives by 30% compared to traditional detection methods. These results underscore the potential of ML and DL models in maintaining cloud stability even during high-impact disaster situations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arunkumar, M., Ashok Kumar, K.: Malicious attack detection approach in cloud computing using machine learning techniques. Soft. Comput. 26(23), 13097–13107 (2022)
Balasubramaniam, S., Vijesh Joe, C., Sivakumar, T.A., Prasanth, A., Satheesh Kumar, K., Kavitha, V., Dhanaraj, R.K.: Optimization enabled deep learning-based ddos attack detection in cloud computing. Int. J. Intell. Syst. 2023(1), 2039217 (2023)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41(1), 23–50 (2011)
Chkirbene, Z., Abdallah, H.B., Hassine, K., Hamila, R., Erbad, A.: Data augmentation for intrusion detection and classification in cloud networks. In: 2021 International Wireless Communications and Mobile Computing (IWCMC), pp. 831–836. IEEE (2021)
Entony. Cloud computing performance metrics (2023)
Li, J., Qu, Y., Chao, F., Shum, H.P.H., Ho, E.S.L., Yang, L.: Machine learning algorithms for network intrusion detection. In: Sikos, L.F. (ed.) AI in Cybersecurity. ISRL, vol. 151, pp. 151–179. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98842-9_6
Reddy, S.S.T., Shyam, G.K.: A machine learning based attack detection and mitigation using a secure SaaS framework. J. King Saud Univ.-Comput. Inf. Sci. 34(7), 4047–4061 (2022)
Sana, M.U., Li, Z., Javaid, F., Liaqat, H.B., Ali, M.U.: Enhanced security in cloud computing using neural network and encryption. IEEE Access 9, 145785–145799 (2021)
Santhosh Kumar, S.V.N., Selvi, M., Kannan, A.: A comprehensive survey on machine learning-based intrusion detection systems for secure communication in internet of things. Comput. Intell. Neurosci. 2023(1), 8981988 (2023)
Sommer, R., Paxson, V.: Outside the closed world: On using machine learning for network intrusion detection. In: 2010 IEEE Symposium on Security and Privacy, pp. 305–316. IEEE (2010)
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and innovation program through the NGI ONTOCHAIN program under cascade funding agreement No 957338, and CRITERION (Cloud paRadigms for vIrTual/augmEnted RealIty cOllaborative eNvironments) Universitá della Campania “L.Vanvitelli” Young Researcher Projects D.R. n. 639/2023 D.R. n. 834/2022.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Capasso, G., Esposito, A. (2025). Detection of DoS Attacks in Cloud Computing: A Machine Learning Approach. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-76452-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-76452-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-76451-6
Online ISBN: 978-3-031-76452-3
eBook Packages: EngineeringEngineering (R0)