Funding

Self-funded

Project code

CMP10181026

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. Mo Adda. 

 

 

 

The work on this project will:

  • Investigate Federated Learning (FL) techniques for distributed anomaly detection in O-RAN networks.
  • Design secure aggregation mechanisms resilient to adversarial and poisoning attacks in O-RAN networks.
  • Evaluate privacy-preserving training strategies using telemetry and log data from edge nodes and RAN controllers.
  • Develop real-time, lightweight models that can be deployed in near-RT RIC environments.

The Open Radio Access Network (O-RAN) framework introduces flexibility and openness to 5G and future 6G systems, enabling modularity and interoperability across different vendors. However, this shift also exposes critical infrastructure to new vulnerabilities and attack surfaces.

This project aims to develop a Federated Learning-based security solution for O-RAN systems to detect anomalies and cyber threats in a distributed, privacy-conscious manner. Instead of relying on centralized intrusion detection systems, the project will explore how machine learning models can be collaboratively trained across distributed RAN components—such as base stations and edge servers—without transferring raw data.

 

Key research objectives include:

  • Designing FL protocols tailored for heterogeneous edge nodes in the RAN.
  • Developing lightweight, communication-efficient models suitable for deployment in real-time environments.
  • Investigating defense strategies against model poisoning, data manipulation, and adversarial attacks in FL scenarios.
  • Evaluating system performance using telecom simulators or emulators like OpenAirInterface or srsRAN.

 

 

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.

 

  • Experience with machine learning and Python programming.
  • Understanding of Federated Learning and distributed training frameworks.
  • Background knowledge of 5G/6G network architecture and O-RAN is desirable.
  • Familiarity with telecom simulation tools is a plus.

 

 

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: CMP10181026