A Review of Machine Learning-based Security in Cloud Computing

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.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Stair climber smart mobile robot (MSRox)

MSRox is a wheeled mobile robot with two actuated degrees of freedom which enables it to have smooth motion on flat surfaces. It has the capability of climbing stairs and traversing obstacles, and adaptability toward uphill, downhill and slope surfaces. MSRox with 82 cm in length, 54 cm in width and 29 cm in height has been designed to climb stairs of 10 cm in height and 15 cm in width; nevertheless, it has the capability of climbing stairs up to about 17 cm in height and unlimited widt. In this paper, the motion systems and the capabilities of MSRox are described. Furthermore, experimental results of stair climbing and a comparison of the results with others are presented

A hybrid contact state analysis methodology for robotic-based adjustment of cylindrical pair

The peg-in-hole insertion and adjustment operation is one of the most common tasks in the robotic and automatic assembly processes. Fine motion strategies associated with adjustment operations on a peg-in-hole are fundamental manipulations that can be utilised in dynamic assembly and reconfigurable workholding or fixturing systems. This paper presents a comprehensive study of robotic-based height adjustment of a cylindrical pair based on maintaining minimum contact forces between the links