Artificial Intelligence Methods for Cryptography in Critical National Infrastructure

Applications are invited for a three-year MPhil/PhD studentship. The studentship will start on 1 October 2021.

To apply please use the online application form. Simply search for PhD Mathematics and Statistic, then clearly state that you are applying for a PhD studentship within the School of Engineering Computing and Mathematics and name the project at the top of your personal statement.

Online application

Take a look at the Doctoral College's general information on applying for a research degree.

Project description

The degree of connectivity in the world is very high and rising fast. However, as advances in technology have come to the fore, so has the chance for misuse, and, increasingly, more modern forms of crime involving networks. Cryptography, the science of sending secure communications, is therefore crucial. 

There is a large body of work on the use of groups to build secure cryptographic primitives, providing extra layers of encryption and obfuscating information passed during key exchange. Many conjectured primitives have been proposed and subsequently broken in the search for suitable candidates over which to compute. The methods to break such primitives range from Monte-Carlo to AI approaches, such as evolutionary algorithms and hyperheuristics, but can demand considerable resource in human and computational time. This interdisciplinary project will investigate groups and primitives which may be invulnerable to such approaches, implementing machine learning approaches to analyse data generated during the break process. It is also sought to understand such primitives and actions by AI algorithms via visualisation, and apply them to cyber-physical situations, particularly in critical national infrastructure. 

This work is timely, due to the identification of cryptography in the National Cybersecurity Strategy as an area for enhancing capability. Several billion pounds are also being invested in defence and AI, both as part of the UK Industrial Strategy as well as future funding, making the field fast-moving, exciting, and readily useable in industrial applications.

Eligibility 

Applicants should have (at least) a first or upper second class honours degree in an appropriate subject and preferably a relevant MSc or MRes qualification. Expertise in programming and machine learning approaches is an advantage. 

The studentship is supported for three years and includes full home tuition fees plus a stipend of £15,609 per annum (2021/22 rate). The studentship will only fully fund those applicants who are eligible for home fees with relevant qualifications. Applicants normally required to cover international fees will have to cover the difference between the home and the international tuition fee rates (approximately £12,697 per annum).

If you wish to discuss this project further informally, please contact matthew.craven@plymouth.ac.uk. However, applications must be made in accordance with the details shown below.

General information about applying for a research degree at the University of Plymouth and to apply for this position please visit: https://www.plymouth.ac.uk/student-life/your-studies/research-degrees

Please mark it FAO Doctoral College and clearly state that you are applying for a PhD studentship within the School of Engineering, Computing and Mathematics.

For more information on the admissions process email the Doctoral College, doctoralcollege@plymouth.ac.uk.

The closing date for applications is 19 April 2021. Shortlisted candidates will be invited for interview the week beginning 3 May 2021. We regret that we may not be able to respond to all applications. Applicants who have not received an offer of a place by 31 May should consider their application has been unsuccessful on this occasion.