|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||From £17,668 Home fees (2023/24) included plus an annual stipend paid at the UKRI rate (award for 2022/23).|
|Placed On:||20th September 2023|
|Closes:||16th October 2023|
A novel energy-efficient spiking neural network for real-time intrusion detection
Project contact: Dr Sergio Davies
Home fees (2023/24) included plus an annual stipend paid at the UKRI rate (£17,668 for 2022/23).
Mode of study: Full time
Open to home and overseas students. Eligible overseas students will need to make up the difference in tuition fees.
Closing date: 16 October 2023
Expected start: January 2024
This groundbreaking research project addresses the pressing need for enhanced network security in our increasingly AI-driven world. As Artificial Intelligence continues 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.
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 developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.
How to apply
Interested applicants should contact Dr Sergio Davies for an informal discussion.
To apply you will need to complete the online application form for a full-time PhD in Computing and digital technology (or download the PGR application form), by clicking the 'Apply' button, above.
You should also complete the PGR thesis proposal (supplementary information) form 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, you will need to upload your statement in the supporting documents section, or email the application form and statement to mailto:PGRAdmissions@mmu.ac.uk.
Closing date 16 October 2023.
Expected start January 2024.
Please quote the reference: SciEng-SD-2023-spiking-neural-network
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