Behavioural Observation for Critical Infrastructure Security Support

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.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Liverpool John Moores University

Year dc:date.issued

2014

Author dc:creator

• Hurst, William

Contributors dc:contributor

• Merabti, M
• Fergus, P

• QA75 Electronic computers. Computer science
• QA76 Computer software

DOI dc:identifier.doi

https://doi.org/10.24377/LJMU.t.00004382

OAI identifier oai:identifier

oai:researchonline.ljmu.ac.uk:4382