Background
Matt's PhD is in the Electronics and Electrical Engineering department of Wolfson School; his research theme is Communications. He began his PhD in January 2019. He is supervised by Dr Kostas Kyriakopoulos and Professor Sangarapillai Lambotharan and is funded by the Doctoral College.
Matt obtained a BSc in Computer Science from De Montfort University (DMU) in 2016. He studied an MSc in Internet Computing and Network Security at Loughborough University and graduated in 2018.
He is interested in the Internet of Things, the interconnection of micro and smart devices. He is a keen programmer who can program efficiently in Python, C and Java and enjoys web development, leading him to code in PHP.
Title of thesis: Detecting multi-stage cyber-attacks using intrusion detection systems and machine learning
As Internet usage, Cloud Computing and Internet of Things (IoTs) continue to grow rapidly, so does the need for more intelligent intrusion detection. IoT, as an example of edge computing, is considered a vulnerable point of network security, which is seen as a significant concern as by 2030, it is anticipated that there will be up to 30 billion IoT devices connected to the Internet.
These interconnected micros and smart devices have left a gap in network security that malicious actors are exploiting. Intrusion Detection Systems (IDS) are one way of detecting such attacks, although they sometimes struggle as these attacks are increasingly pre-panned multi-stage attacks (MSA). These MSAs are challenging to detect as each stage may appear benign, but when all stages are combined become a malicious cyber attack. Further, these attacks may span over days, weeks or even months.
Matt's research aims to detect these network attacks by utilising anomaly-based IDS, with the assistance of classification machine learning algorithms such as artificial neural networks and logistic regression.