Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617387
Title: Behavioural observation for critical infrastructure security support
Author: Hurst, William
ISNI:       0000 0004 5350 4843
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2014
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Abstract:
Critical infrastructures include sectors such as energy resources, finance, food and water distribution, health, manufacturing and government services. In recent years, critical infrastructures have become increasingly dependent on ICT; more interconnected and are often, as a result, linked to the Internet. Consequently, this makes these systems more vulnerable and increases the threat of cyber-attack. In addition, the growing use of wireless networks means that infrastructures can be more susceptible to a direct digital attack than ever before. Traditionally, protecting against environmental threats was the main focus of critical infrastructure preservation. Now, however, with the emergence of cyber-attacks, the focus has changed and infrastructures are facing a different danger with potentially debilitating consequences. Current security techniques are struggling to keep up to date with the sheer volume of innovative and emerging attacks; therefore, considering fresh and adaptive solutions to existing computer security approaches is crucial. The research presented in this thesis, details the use of behavioural observation for critical infrastructure security support. Our observer system monitors an infrastructure’s behaviour and detects abnormalities, which are the result of a cyber-attack taking place. By observing subtle changes in system behaviours, an additional level of support for critical infrastructure security is provided through a plug-in device, which operates autonomously and has no negative impact on data flow. Behaviour is evaluated using mathematical classifications to assess the data and detect changes. The subsequent results achieved during the data classification process were high and successful. Our observer approach was able to accurately classify 98.138 % of the normal and abnormal system behaviours produced by a simulation of a critical infrastructure, using nine data classifiers.
Supervisor: Merabti, M.; Fergus, P. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.617387  DOI: Not available
Keywords: Critical Infrastructure ; Behavioural Observation ; Cyber-Attack ; Security ; Data Analysis ; Data Classification
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