Machine Learning-Driven Threat Detection in Multi-Cloud Environments
DOI:
https://doi.org/10.47941/ijce.3262Keywords:
Machine Learning, Threat Detection, Intrusion Detection Systems, Federated Learning, Cybersecurity, Artificial Intelligence.Abstract
The increasing adoption of multi-cloud environments presents new challenges in maintaining a consistent and robust security posture across heterogeneous platforms. Traditional threat detection systems, often reliant on static rules and signatures, struggle to address sophisticated, distributed, and rapidly evolving cyber threats. This paper investigates the application of machine learning (ML) techniques for dynamic and intelligent threat detection in multi-cloud ecosystems. The study explores a range of supervised, unsupervised, and reinforcement learning models for their efficacy in identifying anomalies, intrusions, and advanced persistent threats (APTs). The paper introduces a federated learning-based architecture that enables decentralized threat intelligence sharing while preserving data privacy across cloud providers. Through experimental evaluation using benchmark datasets such as UNSW-NB15 and CICIDS2017, the study demonstrate that ML-driven approaches outperform traditional intrusion detection systems in terms of accuracy, adaptability, and false positive rates. Furthermore, the study discusses implementation challenges including data heterogeneity, model drift, and regulatory constraints. My findings highlight the transformative potential of ML in enabling proactive and resilient cybersecurity strategies within multi-cloud infrastructures. This research contributes to the development of intelligent, scalable, and privacy.
Downloads
References
[1] Gartner, “Forecast: Public Cloud Services, Worldwide, 2021-2027,” Gartner, 2023.
[2] S. Khan et al., “A survey of intrusion detection and prevention systems,” IEEE Access, vol. 9, pp. 29679–29707, 2021.
[3] M. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[4] J. Kim et al., “Long short-term memory recurrent neural network classifier for intrusion detection,” 2016 International Conference on Platform Technology and Service (PlatCon), pp. 1–5, 2016.
[5] N. Fernando, S. W. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, no. 1, pp. 84–106, Jan. 2013.
[6] T. Ristenpart et al., “Hey, you, get off of my cloud: Exploring information leakage in third-party compute clouds,” Proc. 16th ACM Conf. Computer and Communications Security (CCS), pp. 199–212, 2009.
[7] H. H. Al-Daajeh and A. A. Bakar, “Security challenges and solutions in cloud computing environments: A survey,” IEEE Access, vol. 11, pp. 119387–119405, 2023.
[8] A. D. Kent, “Cybersecurity data sources for dynamic network research,” ACM Computing Surveys (CSUR), vol. 49, no. 3, pp. 1–36, Oct. 2016.
[9] A. H. Lashkari, M. S. I. Mamun, and A. A. Ghorbani, “Characterization of Tor Traffic Using Time Based Features,” Proceedings of the 3rd International Conference on Information Systems Security and Privacy, pp. 253–262, 2017.
[10] S. Yin, H. Ding, A. S. Mohamed, and A. K. Qin, “A deep learning approach for intrusion detection using recurrent neural networks,” IEEE Access, vol. 9, pp. 21928–21937, 2021.
[11] S. M. A. Kazmi, N. Javaid, M. A. Khan, and M. Imran, “Anomaly detection using machine learning in cloud computing: A survey,” IEEE Access, vol. 9, pp. 29698–29716, 2021.
[12] H. Lin, Y. Wang, and Z. Li, “A survey on reinforcement learning for cyber security,” IEEE Access, vol. 8, pp. 131723–131745, 2020.
[13] H. Nguyen, T. T. Nguyen, T. V. Pham, and E. Huh, “Hybrid Deep Learning for Detecting Intrusions in Cloud Datacenter Networks,” IEEE Access, vol. 8, pp. 220898–220909, 2020.
14] B. McMahan et al., “Communication-efficient learning of deep networks from decentralized data,” Proc. of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, pp. 1273–1282, 2017.
[15] C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,” Foundations and Trends in Theoretical Computer Science, vol. 9, nos. 3–4, pp. 211–407, 2014.
[16] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” Proc. of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 1310–1321, 2015.
[17] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, May 2020.
[18] M. S. Hossain and G. Muhammad, “Cloud-assisted industrial internet of things (IIoT)–enabled framework for health monitoring,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7446–7453, Aug. 2020.
[19] N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6, Nov. 2015.
[20] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” ICISSP 2018 - Proceedings of the 4th International Conference on Information Systems Security and Privacy, pp. 108–116, Jan. 2018.
[21] A. Mehmood et al., “A comprehensive survey on security issues in cloud computing: Taxonomies, challenges, and solutions,” ACM Computing Surveys, vol. 54, no. 6, pp. 1–36, June 2022.
[22] M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, “Network anomaly detection: Methods, systems and tools,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303–336, 2014.
[23] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1–19, Mar. 2019.
[24] J. Konečný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” NeurIPS Workshop on Private Multi-Party Machine Learning, 2016.
[25] T. Nishio and R. Yonetani, “Client selection for federated learning with heterogeneous resources in mobile edge,” Proc. IEEE ICC, pp. 1–7, 2019.
[26] E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, vol. 108, pp. 2938–2948, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Sri Ramya Deevi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
