Exploring security protection for machine learning solutions in O-RAN
Applications are now CLOSED
Overview
Network infrastructure is critical to support our digital communications and services. As such, the network is constantly under attack. Network softwarization supports network protection through flexibility, scalability, and the potential for adaptive response to network threats. The open networking approach that emerged with software-defined networking (SDN) is being adopted in the fifth (5G) and sixth (6G) generation cellular networks. The Open Radio Access Network (O-RAN) promotes openness and standardization, increased flexibility through the disaggregation of RAN components, supports programmability, flexibility, and scalability with technologies such as SDN, Network Function Virtualization (NFV), and cloud, and brings automation through the RAN Intelligent Controller (RIC). This intelligence in O-RAN is underpinned by artificial intelligence/machine learning (AI/ML) techniques for network management and optimization. Specifically, the RIC houses third-party applications (rApps and xApps) powered by AI/ML that streamline RAN operations and manage complexity. However, while exploiting the performance benefit of this network intelligence, we cannot neglect the security of the ML implementation. For example, ML-based rApps/xApps must be protected from adversarial attacks.
The main goal of this PhD project is to investigate and derive techniques and algorithms to provide detection and protection against network-based attacks in the O-RAN. An open-source O-RAN testbed will be used for the research.
Funding Information
To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.
To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:
• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.
Candidates from ROI may also qualify for Home student funding.
Previous PhD study MAY make you ineligible to be considered for funding.
Please note that other terms and conditions also apply.
Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.
A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.
Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.
Project Summary
Dr Sandra Scott-Hayward
Full-time: 3 or 3.5 years