Cloud Security Challenges in Indian SMEs: A Machine Learning Approach
DOI:
https://doi.org/10.36676/jrps.v14.i5.1555Keywords:
Cloud Security, Machine Learning, Indian SMEsAbstract
As small and medium enterprises (SMEs) in India increasingly migrate their operations to the cloud, they face significant security challenges. This paper explores how machine learning (ML) techniques can address cloud security issues in Indian SMEs, focusing on threat detection, vulnerability assessment, and anomaly detection. The study evaluates various ML models, including Random Forest, Support Vector Machines (SVM), and KMeans clustering, in detecting security breaches and unauthorized access in cloud environments. Case studies from Indian SMEs in sectors such as retail, manufacturing, and healthcare are presented to highlight the effectiveness of MLdriven cloud security solutions. The paper also discusses the challenges of implementing these technologies, such as data privacy concerns, compliance with Indian cyber laws, and the need for affordable security solutions for SMEs. By offering insights into the potential of ML to secure cloud infrastructures, the paper aims to guide Indian SMEs in enhancing their cybersecurity posture
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