Funding

Self-funded

Project code

COMP6281025

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 Farzad Arabikhan.

 

The work on this project will:

  • Exploration and application of advanced techniques for preprocessing, includes investigating strategies to collect data and preprocessing 5G network data
  • Comprehensive investigation, analysis, and design of Federated Learning Approaches and Algorithms
  • Designing approaches that enhance the efficiency, adaptability, robustness, and sustainability of federated learning models, ensuring optimal performance in cyber threat detection.
  • Simulation, evaluation, and validation of the developed models. The focus extends to real-world datasets, particularly within the realm of cybersecurity.

Context

In today's digital world, traditional methods of dealing with cyber threats are falling short. This PhD opportunity dives into the innovative field of federated learning, aiming to revolutionize how we tackle cyber threats in 5G networks. Instead of relying on a central approach, this project tapping into the combined smarts of devices everywhere, working together to detect and stop cyber threats effectively. The focus on federated learning in this project is about shaking up the cybersecurity game. Old models struggle to keep up with fast-changing digital threats, leaving vulnerabilities that cyber attackers exploit.  Federated learning is adaptable, creating strong systems that evolve as new threats emerge.

This PhD program brings together machine learning and cybersecurity, concentrating on giving network devices the power to work together in threat detection in 5G networks without compromising privacy. We're getting into the details of building algorithms that smoothly handle decentralized data, pulling out valuable insights while keeping sensitive information safe. In this lively research setting, candidates will dig into the many facets of cyber threats. In this project goal is developing techniques to protect privacy, ensuring data stays confidential while still providing useful intelligence. This research involves a comprehensive investigation, analysis, and design of federated learning approaches and algorithms. This project not just stopping at theory; it includes actively designing approaches that boost the efficiency, adaptability, robustness, and sustainability of federated learning models. This ensures optimal performance in the critical task of cyber threat detection.

 

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 Government Doctoral Loan (conditions apply).

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

The entry requirements for a PhD or MPhil include an upper second class honours degree or equivalent in a relevant subject or a master's degree in an appropriate subject. Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.

If English is not your first language, you'll need English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

If you don't meet the English language requirements yet, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.

The ideal candidate will have BSc and/or MSc in Computer science, Electrical engineering or Mathematics. Some background in machine learning, data collection and data engineering, would be beneficial. Working knowledge of Python programming language and ML libraries like PyTorch, Tensorflow or Keras is preferable and potential candidates should have a clear interest in working on both fundamental and application aspects of this research.

 

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: COMP6291025