Machine vs Non-Machine Learning Approaches to Cloud Security Solutions: A Survey
Gopal Krishna Shyam, Doddi Srilatha · International Journal of Engineering and Technology · 2019
Cloud computing is a trending paradigm that provides both physical and logical computational resources as services over the Internet.The basic advantages of the cloud are, a reduction of IT organization's infrastructure cost, flexibility to access and use the services.Regardless of its advantages, it has raised several security concerns such as data availability, data privacy, data location, authentication, authorization, access control, network security, web security, and virtual machine security etc. which may potentially hamper its growth.In recent years, the expansion of several types of dynamic threats such as data breaches, account hijacking, insecure interfaces, advanced persistent threats, shared technology vulnerabilities, and distributed denial of service attacks target the cloud to disrupt cloud services and can compromise security.To tackle several security issues, solutions can be provided through a set of control based technologies such as next generation firewalls, cryptography techniques, intrusion detection systems, software defined networks, machine learning techniques etc.In this paper, we focus on comparative analysis of several cloud security issues through machine learning and non-machine learning approaches.Some open challenges for further research have also been suggested.