Kapasa, R (2020) RISK ANALYSIS AND MANAGEMENT OF SECURITY THREATS IN VIRTUALISED INFORMATION SYSTEMS USING PREDICTIVE ANALYTICS. Doctoral thesis, Liverpool John Moores University.

Preview

Text KapasaPhD2020.pdf - Published Version Download (5MB) | Preview

Abstract

The use of online server applications has increased in recent years. To achieve the benefits of these technologies, cloud computing, with its ability to use virtual machine technologies to overcome limitations and guarantee security and quality of service to its end user customer, is being used as a platform to run online server applications. This however brings about a number of security issues aimed specifically at virtual machine technologies. A number of security solutions like virtual machine introspection, intrusion detection and many more, have been proposed and implemented, but the question to combat security issues in near or even real time still remains. To help answer the above question or even move a step further from the existing solutions, which still use data mining techniques to combat the security issues of virtualisation, we propose the novel use of predictive analytics for risk analysis and management of security threats in virtualised information systems as well as design and implement a novel predictive analytics framework used to design build and implement the same predictive analytics model In this project, we adopt the use of predictive analytics and demonstrate how it can be used for managing risks and security of virtualised environments. An experimental testbed for the simulation of attacks and data collection is set-up. Exploratory data analytics process is carried out to prepare the data for predictive modelling. A linear regression predictive model is built using the results from the exploratory data analytics using linear regression algorithm. The model is then validated and tested for predictive accuracy using Naïve Bayes and logistic algorithms respectively. Time series algorithms are then used to build a time series predictive model that will predict attacks (DoS attacks in this case) in real time using new data. Designing and implementing the proposed predictive analytics model, which is aimed at monitoring, analysing and mitigating security threats in real time successfully demonstrates the use of predictive analytics modelling as a security management tool for virtualised information systems as a novel contribution to virtualisation security.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	predictive analytics; virtualisation security; cloud computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Date Deposited:	17 Nov 2020 09:42
Last Modified:	08 Nov 2022 14:10
DOI or ID number:	10.24377/LJMU.t.00013994
Supervisors:	Forsyth, H and Laws, A
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13994

View Item