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Analysis of malware evolution using phylogenetic networks (MOULTONVU19SF)

Project Description

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
Start date: October 2019
Study mode: Full time PhD

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. The amount charged annually will vary considerably depending on the nature of the project and applicants should contact the primary supervisor for further information about the fee associated with the project.


9. References to be included in advertising (up to 5)
i) 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.
ii) 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.
iii) 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.

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