AI-Driven Cloud Security: Advancing Sustainable and Zero Trust Frameworks through Automation
About the Project
We are seeking a PhD candidate to join our cutting-edge research projects focused on developing innovative solutions to enhance cyber security in cloud environments and edge computing platforms. These solutions involve AI-driven continuous monitoring systems designed for real-time security incident detection in zero trust environments. The PhD research will primarily involve developing and evaluating deep learning-based algorithms and techniques that can continuously monitor the behaviour of users, devices, and networks in real-time to detect and mitigate potential security incidents and respond to them before they cause any harm.
The candidate will explore Generative AI models to improve the identification performance and develop novel approaches to address key security challenges in anomaly detection for user and device behaviour, network traffic, and application usage patterns specifically within data centres, cloud environments, and edge computing platforms.
As a member of research team, the candidate will have access to state-of-the-art research facilities and collaborate with a highly interdisciplinary team of researchers. The candidate will also have opportunities to present their research findings at top-tier conferences and publish in high-impact academic journals.
Entry requirements:
The ideal candidate should have or expect to achieve at least a 2:1 Honours degree (or equivalent) in Computer Science, Cyber Security, or a related subject. A relevant master’s degree and/or experience in AI will be an advantage. The applicant should also have strong programming skills in machine learning and more particularly deep learning (PyTorch, TensorFlow and, Keras,) is an asset, and be familiar with C/C++, various scripting languages and with the Linux environment.
Qualified applicants are encouraged to contact Dr. Xing Liang (X.Liang@kingston.ac.uk) to discuss their application.
Funding Notes
there is no funding for this project
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