Paper Title
Harnessing Machine Learning for Cloud Security Threat Identification

Abstract
Cloud computing's meteoric rise has triggered a spending spree, with companies investing heavily in both internal use and external services. But with great power comeCloud computing's rapid adoption has led to significant investments in both proprietary and third-party services. However, this growth also introduces substantial security challenges for businesses and users alike. Machine learning (ML) has become a crucial tool in enhancing cloud security. This paper conducts a Systematic Literature Review (SLR) of 63 studies to explore the integration of ML in cloud security. It identifies three primary research areas: detecting various cloud threats, including data breaches and distributed denial-of-service (DDoS) attacks, which account for 16% and 14% of threats respectively. A range of 30 ML techniques is employed, with hybrid and standalone models like Support Vector Machine (SVM) showing significant promise. About 60% of the reviewed studies evaluate their models using 13 different performance metrics, such as the true positive rate and training time. The KDD and KDD CUP'99 datasets are frequently used for training these models. This review highlights the evolving role of ML in cloud security, suggesting that more advanced ML techniques will continue to emerge, fortifying the cloud against ever-evolving threats. Keywords - Cloud Security; Artificial Intelligence (AI) Methods; Data Protection Violations; DDoS Attacks; Support Vector Machines (SVMs).