Analysis of malware evolution using phylogenetic networks (MOULTONVU19SF)

Just as with biological viruses, malware, or malicious software evolves. For example, malware will often be created by combining and evolving existing code, a process that is analogous to recombination in biological viruses. Therefore, it is of interest to develop systematic approaches that can be used to reconstruct evolutionary histories for malware to, for example, understand the origins of the malware, or even to predict its potential future evolution. Recently, there has been great interest in using techniques from phylogenetics to analyse malware evolution. Initially, the focus was on constructing trees to represent malware evolution, but there is now an increasing interest in using phylogenetic networks as well, since these can be used to represent processes which trees are not able to capture. In this PhD project we will develop models and approaches to analysing malware evolution using phylogenetic networks. The student will work with a team of experts in phylogenetics (Moulton) and cyber security (Buckley).

Applicants should have a strong background in algorithm development and have excellent programming skills.

Entry requirements: The standard minimum entry requirement is 2:1, acceptable first degrees include Computer Science or equivalent.
For more information about the supervisor of this project, please visit https://people.uea.ac.uk/en/persons/v-moulton
Start date: October 2019
Study mode: Full time PhD