Machine Learning Algorithms for Cloud Computing Security: A Review

Abstract

: Cloud computing has revolutionized the way businesses and individuals access and manage IT resources. However, cloud computing also introduces new security challenges. Cloud-based systems are often more complex and distributed than traditional on-premises systems, making them more vulnerable to attack.Machine learning (ML) is a powerful tool that can be used to analyze cloud computing attacks and improve cloud security. ML algorithms can be trained on large datasets of attack data to learn the patterns and characteristics of different types of attacks. Once trained, ML models can be used to detect new attacks in real time and recommend mitigation strategies. This paper reviews the state of the art in using ML to analyze cloud computing attacks. It discusses the different ways that ML can be used to improve cloud security, including developing intrusion detection systems (IDS), anomaly detection systems, and attack forecasting systems. The paper also discusses the challenges of using ML for cloud security, such as the need for large and high-quality training data and the need to adapt ML models to new and emerging threats.The paper concludes by discussing the future of using ML for cloud security. It argues that ML is a key technology for improving cloud security in the face of increasingly sophisticated and targeted attacks.