Developing Next-Generation Attack Surface Mapping Techniques

About the Project

EPSRC Doctoral Landscape (DLA) PhD Opportunity:

Developing Next-Generation Attack Surface Mapping Techniques

We invite applications for a fully funded PhD studentship at Lancaster University in collaboration with SP Electricity North West (SP ENWL). This is an exciting opportunity to develop next generation methods for attack surface mapping, exploring how data science, AI, and cybersecurity techniques can be used to produce more accurate and reliable tools that support decision-makers in their analysis of large-scale modern digital infrastructure, such as power grids.

PhD Overview

As society becomes increasingly reliant on digital infrastructure, it is critical that decision-makers at organisational and national levels understand the resilience of their systems. Analysts use Attack Surface Mapping (ASM) to identify their internet-connected digital assets and associated vulnerabilities. This allows them to understand how robust the infrastructure is, plan mitigation strategies, and support recovery post-attack.

This PhD will leverage data-science, AI, and cybersecurity techniques to develop the next generation of ASM tools. Research will include:

• Fusing multiple ASM tools and pieces of open-source information to give more accurate understanding of attack surfaces than the current state-of-the-art tools can provide.
• Developing techniques to measure and interpret the uncertainty of ASM results, giving practitioners confidence in their analysis.
• Investigating how AI automation can safely and effectively improve the ASM process.

This PhD is in collaboration with SP Electricity North West, with a crucial focus on securing digital infrastructure across their network and enabling the secure deployment of innovative new services as they digitise their operations. Furthermore, this project aligns with ongoing work the team are carrying out with the UK’s National Cybersecurity Centre (NCSC); as such, there is a real opportunity for your research to make an impact.

Supervisory Team

• Dr Edward Austin (School of Computing and Communications)
• Prof Nicholas Race (School of Computing and Communications)
• Dr Xiandong Ma (School of Engineering)

Training and Development

The successful candidate will receive a tailored training programme including:

• Support using, and access to, ASM tools such as Shodan and Censys.
• Opportunities to engage with national and international conferences, workshops, and training events.
• Insight into the power sector through industrial collaboration with SP ENWL.

Funding

• A 3.5-year UKRI-funded studentship, including a stipend (currently £20,780 per year) and full tuition fees for Home students.
• An additional research training grant (£1,000 per year) for consumables, maintenance, and travel to events/conferences.

Eligibility

• Applicants should have (or expect to obtain) a First or Upper Second-Class degree (or equivalent) in Computer Science, Data Science, or Cybersecurity. Applicants from other disciplines with a substantial mathematical component are also encouraged to apply.
• There is no expectation that a candidate will be proficient in all areas of data science, cybersecurity, computer networking and AI tooling. However, candidates should be aware that this PhD will have a substantial cybersecurity component.

Application Process

Applicants should submit:

1. A cover letter outlining their motivation and suitability.
2. A CV outlining skills and experience.

Applications will be considered on a rolling basis until the position is filled. The expected start dates are either April 2026 or October 2026.

Contact Information

Please contact Prof Nicholas Race (n.race@lancaster.ac.uk) & Dr Edward Austin (e.austin@lancaster.ac.uk)

Funding Notes

• A 3.5-year UKRI-funded studentship, including a stipend (currently £20,780 per year) and full tuition fees for Home students.
• An additional research training grant (£1,000 per year) for consumables, maintenance, and travel to events/conferences.