International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
www.irjet.net
Comparative Review of Supervised vs. Unsupervised Learning in Cloud Security Applications Narinder Singh Kharbanda -----------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract Cloud computing environments are increasingly targeted by sophisticated cyber threats, necessitating robust security measures. Machine learning (ML) has emerged as a powerful tool in enhancing cloud security, with both supervised and unsupervised learning techniques being widely employed. This review paper provides a comprehensive comparative analysis of supervised and unsupervised learning approaches in the context of cloud security. We examine the strengths and weaknesses of each approach, focusing on their effectiveness in threat detection, anomaly detection, and response automation. By evaluating various algorithms, methodologies, and performance metrics, we highlight how these techniques are applied in real-world scenarios. The review also discusses the trade-offs between model accuracy, scalability, and interpretability, offering insights into their suitability for different cloud security tasks. The paper concludes with recommendations for selecting appropriate learning methods based on specific security requirements and challenges.
Keywords: Cloud Security, Machine Learning, Supervised Learning, Unsupervised Learning, Threat Detection 1. Introduction Cloud computing has revolutionized the way organizations store, process, and manage data. The global cloud computing market size was valued at USD 369.25 billion in 2021 and is projected to reach USD 1,554.94 billion by 2030, growing at a CAGR of 17.5% from 2022 to 2030 [1]. However, this paradigm shift has also introduced new security challenges. The distributed nature of cloud environments, coupled with their multi-tenant architecture, creates a complex landscape for security professionals to navigate. Some of the key challenges include:
© 2024, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 186