AI-driven anomaly detection in cloud computing environments

Chukwuemeka Nwachukwu 1, *, Kehinde Durodola-Tunde 2 and Chukwuebuka Akwiwu-Uzoma 3

1 Department of AI and Research, University of Bradford, UK.
2 Department of AI, Product Management, MoniMoore, London, UK.
3 Department of Computing, University of Dundee, Dundee, United Kingdom.
 
Review
International Journal of Science and Research Archive, 2024, 13(02), 692–710.
Article DOI: 10.30574/ijsra.2024.13.2.2184
Publication history: 
Received on 03 October 2024; revised on 09 November 2024; accepted on 11 November 2024
 
Abstract: 
The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.
 
Keywords: 
Cloud Computing; Anomaly Detection; Artificial Intelligence; Machine Learning; Deep Learning; Security
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0