Improving EV critical infrastructure resilience through AI-powered IoT system vulnerability assessment
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
October and February
Applications accepted all year round
Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.
The PhD will be based in the School of Computing and will be supervised by Dr Benjamin Aziz.
The work on this project could involve:
- Research in AI methods and IoT models and network platforms
- Use and possible development of software tools related to AI and IoT domains
- Research in AI algorithms and mathematical theories
At the core of most global energy strategies nowadays is the goal of developing smart infrastructures that can harness clean energy ecosystems. Among such smart infrastructures are Electric Vehicle (EV) support infrastructures, which aim at providing information to the vehicle drivers in relation to points of charging, traffic events and other road services. EV support infrastructures are largely IoT-based, equipped with backend servers that collect and process often large amounts of data of high variety.
One of the challenges facing any large-scale infrastructure is its resilience against external malicious attacks or internal failures. Resilience depends on the identification and assessment of vulnerabilities, and it is measured through well-defined metrics that can be monitored and trended over time. These are beyond the typical cybersecurity remit, characterised by 3As – Authentication, Authorisation, and Accounting. The rapidly growing big data flows between different IoT devices (charging, payment, information services etc), cloud and edge computer systems, and the intermediate third-party managed services/apps vastly expand the attack surfaces available, thus exposing the EV infrastructure to cyberthreats. This is in many ways, not a unique problem facing EVs. However, without resilient defenses and countermeasures continuously evolving in pace with emerging threats, the uptake of new mobility services will be hampered.
This project aims at exploring AI techniques in the analysis of EV smart infrastructure data, with the purpose of providing a more robust journey experience for EV drivers. We will focus on developing new methods of analysing cyber vulnerabilities of data flows, for example, using distributed federated learning to proactively monitor and identify abnormal behaviors in the data flows and storage systems and developing blockchain based data aggregation to improve transaction assurance in multiparty interactions.
The project will be carried out in collaboration with Dr Erica Yang, Chilton Computing, an independent software design and engineering business specialising in AI-powered Internet of Things (IoT) systems.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2021/2022 fees (applicable for October 2021 and February 2022 start)
PhD and MPhil
Home/EU/CI full-time students: £4,500 p/a**
Home/EU/CI part-time students: £2,250 p/a**
International full-time students: £17,600 p/a*
International part-time students: £8,800 p/a*
PhD by Publication
External candidates: £4,407*
Members of staff: £1,720
All fees are subject to annual increase. If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university or a Master’s degree in computing or computing-related topics such as engineering or mathematics. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
How to apply
When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
When applying please quote project code: COMP5371021