Abstract
M.Sc. (Information Technology)
The purpose of the research presented in this dissertation is to explore the uses of an adaptive multi-agent system for critical information infrastructure protection (CIIP). As the name suggests, CIIP is the process of protecting the information system which are connected to the infrastructure essential to the continued running of a country or organisation. CIIP is challenging due largely to the diversity of these infrastructures.
The dissertation examines a number of artificial intelligence techniques that can be applied to CIIP; these techniques range from multi-agent systems to swarm optimisation. The task of protection is broken into three distinct areas: preventing unauthorised communication from outside the system; identifying anomalous actions on computers within the system; and ensuring that communication within the system is not modified externally.
A multi-agent learning model, MALAMANTEAU, is proposed as a way to address the problem of CIIP. Due to various problems facing CIIP, multi-agent systems present good opportunities for solving these many problems in a single model. Agents within the MALAMANTEAU model will use diverse artificial and computational intelligence techniques in order to provide an adaptable approach to protecting critical networks.
The research presented in the dissertation shows how computational intelligence can be employed alongside multi-agent systems in order to provide powerful protection for critical networks without exposing further security risks.