Machine learning approach to identifying side channel attacks on cloud computer systems

Applications are invited for a three year PhD to commence in October or February.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Mo Adda and Dr Alex Gegov.

Side Channel attacks are the actions of stealing sensitive data by exploiting the vulnerability of a system. The initial work in this direction has successfully lead to the detection of side channel attacks based on Flush+Reload techniques against Advanced Encryption Standard (AES) algorithms. This project aims to develop a new universal framework to address all known side channel attacks mainly Flush+Reload, Prime+Probe, Flush+Flush and Evict+Reload techniques machine learning and statistical approaches.

Fees and funding

Visit the research subject area page for fees and funding information for this project.
Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.