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Analysis of software evolution using phylogenetic networks (MOULTONVU20SF)

Project Description

Just as with biological viruses, malware 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 software 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 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). The student should have a strong background in data analysis and have strong programming skills.

This is a PhD programme.

More information on the supervisor for this project:

The start date of the project is October 2020.

The mode of study is full-time.

Entry requirements:
Acceptable first degree in Computer Science or equivalent
The standard minimum entry requirement is 2:1.

Funding Notes

This PhD project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found at View Website.

A bench fee is also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.


i) Recovering normal networks from shortest inter-taxa distance information, M.Bordewich, K.Huber, V.Moulton, C.Semple, Journal of Mathematical Biology, 77(3), 2018, 571-594.
ii) Reconstructing phylogenetic level-1 networks from nondense binet and trinet sets, K.Huber, L. van Iersel, V.Moulton, C.Scornavacca, T.Wu, Algorithmica, 77(1), 2017, 173-200.
iii) SPECTRE: a Suite of PhylogEnetiC Tools for Reticulate Evolution, S.Bastkowski, D.Mapleson, A.Spillner, T.Wu, M.Balvociute, V.Moulton, Bioinformatics, 1, 2017, 2.
iv) Automated Insider Threat Detection System Using User and Role-Based Profile Assessment, P.Legg, O.Buckley, M.Goldsmith, S.Creese, IEEE Systems Journal 11 (2) 2017, 503-512.
v) Reconstructing what you said: Text Inference using Smartphone Motion, D.Hodges, O.Buckley, IEEE Transactions on Mobile Computing.18,2019, 947-959.

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