The Role of Machine Learning in Predicting and Preventing Cloud Security Threats
Amrita Raj, Akhilesh Kumar Gond, Yakub Alam · International Scientific Journal of Engineering and Management · 2025
This article explores the role of Machine learning in predicting and preventing future cyber-attacks, examining the techniques, applications, Benefits, Challenges, and Future treads in leveraging machine learning for proactive defence. Machine learning has remerged as a powerful tool to enhance cyber security strategies, offering the ability to predict, detect and prevent cyber-attacks with greater accuracy and efficiency. The popularity and usage of cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. Machine learning algorithms can learn from historical attack data, enabling the prediction of future threats and the development of more effective defence mechanisms. Moreover, AI enhanced Authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches. Machine learning examines how ML Models, such as supervised and unsupervised learning, and anomaly detection, are utilized to identify cyber threats, enhance risk assessment and enable proactive security strategies. With the increasing demand and popularity in the usage of cloud computing, there has been a necessity to prevent common attacks and security threats to cloud computing services. Over the past few years, ML techniques have been shown to prevent as well as detect security attacks on the cloud. In this paper, we provide a comprehensive and systematic literature review on the use of ML in cloud security and its applications and techniques to prevent security issues on cloud computing.