Funding

Self-funded

Project code

CMP10021025

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 Assoc. Prof. Stavros Shiaeles, Dr Aikaterini Kanta and Dr Bander Al-rimy.

The work on this project could involve:

  • Integration of Generative AI: Leveraging GANs and Transformer-based architectures to enhance IPS and SIEM capabilities.
  • Dynamic Anomaly Detection: Training models on diverse datasets to identify subtle anomalies and adapt to new attack vectors.
  • Log File Analysis: Generative AI analyzing log files from various sources (firewalls, servers, applications, endpoints) to correlate events and identify security incidents.
  • Actionable Insights for Administrators: Providing critical updates and suggestions for threat mitigation and security improvements.

This PhD project explores the integration of generative AI into intrusion prevention systems (IPS) and Security Information and Event Management (SIEM) platforms to revolutionize cybersecurity. The focus is on leveraging generative models, such as Generative Adversarial Networks (GANs) and Transformer-based architectures, to enhance detection, prediction, and mitigation of cyber intrusions, as well as to analyze log files from diverse sources, providing administrators with crucial updates and actionable suggestions.

Generative AI can synthesize realistic data instances, simulate various attack scenarios, and develop robust defensive mechanisms. By training these models on extensive datasets of network traffic, including both benign and malicious activities, the IPS can dynamically identify subtle anomalies and adapt to new attack vectors, reducing vulnerability windows typical in static defenses.

This project will utilize GANs to generate synthetic attack data, augmenting existing datasets and refining detection algorithms. Transformer models will analyze temporal patterns in network traffic, offering deeper insights into cyber-attack progressions and enabling timely interventions. Additionally, generative AI will analyze log files from firewalls, servers, applications, and endpoint devices, correlating events to identify potential security incidents. This analysis will allow the SIEM to provide vital updates and actionable suggestions for threat mitigation and security enhancement.

This project aims to demonstrate the feasibility and effectiveness of generative AI in intrusion prevention and SIEM, setting new benchmarks for adaptive defense systems and paving the way for future cybersecurity innovations.

 

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

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in computer science or a related area. 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.

You must have skills in LLM, Generative AI, Cyber Security, Python programming, Machine Learning.

Desirable skills in cloud computing, Linux and networks.

 

 

 

How to apply

We’d encourage you to contact Assoc. Prof. Stavros Shiaeles  (stavros.shiaeles@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: CMP10021025