Industry-Funded PhD opportunity on Machine Learning in Cybersecurity
A fantastic PhD opportunity in LU and Darktrace, leading company in cybersecurity.
Apply at: https://www.lboro.ac.uk/study/postgraduate/research-degrees/phd-opportunities/darktrace-studentships/
Title: “Advanced machine learning approaches against cyber-threats in cyber-physical systems”
In summary:
· Cybersecurity using Machine Learning, LLM
· 3.5 years scholarship
· 25% higher stipend than normal UKRI stipends
· Work in Loughborough University and Darktrace
In more detail:
Resilient cybersecurity relies on people, processes, and technology and this is particularly true in cyber-physical environments, such as in Industry 4.0. In such environments cyber-threat susceptibility and risk remediation require a diverse, collaborative cohort of experts with different background. Some of these stakeholders are experts focusing on the cyber domain and others on the physical domain. Due to the multiple stakeholders, deriving mitigation procedures against cyber attacks within such environments is complex, but of upmost importance for the industrial sector.
To address the above challenge, this PhD project is positioned at the cross-section of the most exciting, growing, and influential technological concepts. Specifically, the project aims to explore the use of Large Language Models (LLM) in tandem with Causal Inference, both integrated in a Reinforcement Learning (RL) framework. The ultimate goal is to enable training autonomous decision-making agents that learn to defend cyber-physical environments under their respective constraints and unique challenges.
The successful PhD candidate will investigate new machine learning approaches leveraging models that capture the cause-and-effect dynamics between different domain knowledge of human experts. Furthermore, LLMs will be used to address the semantic dissonance and priority conflict among experts and optimise agents when deciding on action deployment towards risk remediation in critical environments.
The academic consortium has a strong track record in cyber-security projects, funded by Dstl/Ministry of Defence, and prior success in licensing fundamental research to commercial companies operating in the defence and other sectors.
The PhD candidate would have a great opportunity to be trained and informed by both Loughborough University academics and industrial experts at Darktrace. The candidate will also benefit from a generous stipend that covers 3.5 years, with opportunities for funded placement/visits at Darktrace and prominent international conferences.
A successful candidate is expected to demonstrate strong understanding in Machine Learning fundamentals and be confident in programming skills and Machine Learning packages.
For questions please contact Dr. Kyriakopoulos (elkk@lboro.ac.uk) and cc Dr Gong (Y.Gong@lboro.ac.uk).