Funding

Self-funded

Project code

CMP10171026

Department

School of Computing

Start dates

October, February and April

Application deadline

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. Rahim Taheri and Dr. Ella Haig.

 

 

The work on this project will:

  • Investigate the integration of Large Language Models (LLMs) for threat detection, prediction, and mitigation in 5G/6G networks.
  • Design an AI-enhanced security framework using LLMs for anomaly detection at the edge.
  • Evaluate LLM-based reasoning for real-time decision-making within RAN Intelligent Controllers (RICs).
  • Propose an explainable LLM framework for zero-trust architectures in next-gen networks.

The rapid expansion of 5G and emerging 6G networks introduces unprecedented levels of connectivity, bandwidth, and low-latency services. However, this evolution also increases the attack surface for cyber threats, particularly in the context of open, virtualized, and software-defined network infrastructures. Traditional rule-based and even conventional ML-based security models often fall short in adapting to the dynamic and distributed nature of these environments.

 

This PhD project explores the use of Large Language Models (LLMs) for context-aware security in next-generation networks. By using the generative and reasoning capabilities of LLMs, the project aims to enhance the detection and understanding of complex cyber threats across multiple layers (radio, transport, application). It will focus on developing a lightweight security solution that can operate efficiently in distributed environments like the RAN Intelligent Controller (RIC) and edge nodes.

 

The project will also investigate secure data sharing and model training methods to ensure privacy and reduce communication overheads. Key components include anomaly detection, adversarial robustness, attack attribution, and automated incident response, all supported by LLM-driven analysis of structured and unstructured network logs.

 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only).

 

Bench fees

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.

Entry requirements

A good first degree (minimum upper second class or equivalent) or a Master’s in Computer Science, Cybersecurity, Telecommunications, or a related area. Exceptional candidates with relevant industry experience may also be considered.

English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

 

Strong understanding of Machine Learning, Cybersecurity, and Network Architecture.
Experience with Python programming, NLP/LLMs, and 5G/6G technologies is desirable.

 

 

How to apply

We’d encourage you to contact Dr Rahim Taheri (rahim.taheri@port.ac.uk) to discuss your interest before you apply, quoting the project code.

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: CMP10171026