A Review of Machine Learning Algorithms for Cloud Computing Security

Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edg...

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Bibliographic Details
Published inElectronics (Basel) Vol. 9; no. 9; p. 1379
Main Authors Butt, Umer Ahmed, Mehmood, Muhammad, Shah, Syed Bilal Hussain, Amin, Rashid, Shaukat, M. Waqas, Raza, Syed Mohsan, Suh, Doug Young, Piran, Md. Jalil
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
Access control
Algorithms
Cloud computing
Computer networks
Data centers
Data storage
Distributed processing
Edge computing
Information storage
Internet
Machine learning
Malware
Privacy
Researchers
Servers
Software services
Threat evaluation
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Summary:Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9091379