A Review of Machine Learning-based Security in Cloud Computing
Aptin Babaei Parham M. Kebria Mohsen Moradi Dalvand Saeid Nahavandi
Cloud Computing (CC) is revolutionizing the way IT resources are delivered to
users, allowing them to access and manage their systems with increased
cost-effectiveness and simplified infrastructure. However, with the growth of
CC comes a host of security risks, including threats to availability,
integrity, and confidentiality. To address these challenges, Machine Learning
(ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the
need for human intervention in identifying and resolving security issues. With
the ability to analyze vast amounts of data, and make high-accuracy
predictions, ML can transform the way CSPs approach security. In this paper, we
will explore some of the most recent research in the field of ML-based security
in Cloud Computing. We will examine the features and effectiveness of a range
of ML algorithms, highlighting their unique strengths and potential
limitations. Our goal is to provide a comprehensive overview of the current
state of ML in cloud security and to shed light on the exciting possibilities
that this emerging field has to offer.