PhD Studentship - A Novel Energy-Efficient Spiking Neural Network for Real-Time Intrusion Detection

This is a full-time, funded PhD opportunity, open to both home and overseas students. Please note that only home fees will be covered - eligible overseas students will need to make up the difference in tuition fee funding.

This opportunity provides an annual stipend of the research council minimum rate (set by UKRI) of £19,237 for 2024/25.

Please note that the expected start date for home students is October 2024 and international students January 2025.

Project contact

Dr Sergio Davies

Project advert

This groundbreaking research project addresses the pressing need for enhanced network security in our increasingly connected world. As artificial intelligence (AI) and interconnectivity continue to reshape industries, the demand for robust network security has never been more critical.

Traditional methods struggle with real-time network traffic, especially on lightweight, low-power, resource-constrained edge devices. This project aims to explore the potential of spiking neural networks (SNNs) to revolutionize intrusion detection systems (IDSs) in such devices.

Inspired by the efficiency of biological neural networks, this project offers a unique opportunity to pioneer real-time threat detection solutions. By developing and training an innovative SNN architecture specifically tailored for IDS applications, this project aims at advancing AI, neuromorphic computing, and cybersecurity domains.

Project aims and objectives

• Revolutionise Network Security: Leverage the rapid growth of AI and explore how Spiking Neural Networks (SNNs) can enhance intrusion detection systems (IDSs) to ensure safer network communications.
• Enhance Lightweight Devices: Address the challenges faced by lightweight, low-power, and resource-constrained edge devices in handling real-time network traffic.
• Advance Biologically-Inspired Computing: Drawing inspiration from biology, this project pioneers a cutting-edge platform for streamlined and efficient computing.

Specific requirements of the project

Candidates must have a strong motivation for research and excellent programming skills. Expertise of Spiking Neural Networks and of their training methodologies would be desirable, with an interest in computer network traffic analysis.

Qualifications

• An undergraduate degree (first class or upper second) in Computer Science
• A MSc level in Computer Science or related field would be desirable

Skills

• Knowledge of software development and programming (ideally in python or C/C++)
• Knowledge of neural networks and machine learning algorithms would be beneficial
• Good communication and writing skills
• Self-motivated to conduct research activities independently
• Able to work as part of a joint academia and industry team

How to apply

Interested applicants should contact Dr Sergio Davies  for an informal discussion.

To apply, please complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal  and Narrative CV addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, please upload your statement in the supporting documents section, or email the application form and statement to PGRAdmissions@mmu.ac.uk. Closing date [closing date]. Expected start date [start month/year].

Expected start: Home students October 2024. International students January 2025.

Please quote the reference: SciEng-SD-2024-intrusion-detection

Find out more

Email address: Dr Sergio Davies

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