Overview
The crypto algorithm is safe in theory, but their implementations still leak information to side-channel and can be attacked with Differential Power Analysis (DPA), Correlation Power Analysis (CPA) and state-of-the-art machine learning (ML). Even though SCA countermeasure method like masking is applied to make the implementations safer under DPA and CPA, ML can learn the features of the mask and the sensitive data and be able to attack the protected implementations. The training and attacking process of ML does not show what and where are those features come from, and so cannot explain how a ML model can successfully combine those leakage in analysis. It leads to the requirement of explainable ML models to identify leakage locations that are combined in the attack so that additional countermeasure methods can be applied to the correct executions to strengthen the security of crypto under SCA.
SCA is proved a real threaten to all cryptographic devices. There are a number of ML models developed to attack Advanced Encryption Standard (AES) and PostQuantum CRYSTALS-Kyber (Kyber) implementation. Even though those attacks show the successful results, they did not explain why they had that successful as well as where are leakages come from in SCA countermeasure designs.
This project will :
[1] Evaluate available crypto implementations (aims to AES and Kyber) under ML-based SCA.
[2] Develop tracking mechanism in ML for the source of leakage features.
[3] Build ML model with tracking mechanism to explain the leakage and locations.
[4] Suggest protections method to fulfil those leakages.
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.
How to Apply
Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.
Find a supervisor
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To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.
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