• Malvin Ojong Ndipawoh
• Er. Rupali Sharma

With the increasing reliance on cloud computing, ensuring the security of sensitive data in cloud environments has become a paramount concern. Traditional security measures often struggle to keep pace with the dynamic and evolving nature of cyber threats. This thorough review investigates how cloud security intersects with machine learning, offering a deep analysis of machine learning techniques used for threat detection in cloud environments. Our goals involve understanding the current state of threat detection, evaluating how well machine learning techniques work in this context, and pinpointing areas where existing research can be strengthened and advanced. The review encompasses a diverse range of methodologies, from supervised learning to ensemble methods, shedding light on their strengths and limitations. By critically analyzing existing studies and pinpointing research gaps, this review aims to provide insights that can guide future investigations and contribute to the continuous development of strong and effective security solutions in cloud computing.

Machine Learning, Threat Detection, Cloud Environments, Cybersecurity, Anomaly Detection, Security Frameworks.

"Machine Learning Techniques for Threat Detection in Cloud Environments: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f320-f326, May-2024, Available :http://www.jetir.org/papers/JETIR2405535.pdf

"Machine Learning Techniques for Threat Detection in Cloud Environments: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf320-f326, May-2024, Available at : http://www.jetir.org/papers/JETIR2405535.pdf