User and Entity Behavior Analytics has been one of the key steppingstones towards protecting valuable data and information for organizations that decided to store their assets in a cloud-based storage. The implications suggest that third-party software may not be as reliable as these are prone to attacks due to its accessibility and vulnerable nature. This study aims to provide a comprehensive review that covers User and Entity Behavior Analytics (UEBA) and other machine learning techniques to effectively mitigate cyberattacks and protect organizational assets from cloud-based security threats. The objective is to identify and analyze anomalous user and entity behaviors that may indicate potential cyberattacks and provide recommendations for organizations to enhance their cloud security posture and minimize the risk of data breaches and other security incidents. The research covers some of the algorithms used in detecting anomalies such as Isolation Forest, Deep Autoencoder, and Linear Regression, and emphasizes the adaptability of these chosen algorithms to the dynamic landscape of cyber threats, especially in cloud environments. By arriving at a cohesive integration of machine learning algorithms, this study advocates for a holistic approach that aligns with evolving security challenges. Ultimately, the significance lies in offering a nuanced perspective on effective cyber threat mitigation strategies, contributing to the broader conversation on securing organizational assets in the face of evolving cybersecurity landscapes.
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