PhD Studentship - Secure computing systems based on stochasticity of RRAM (1263120FP)

Supervisor:                 Alexander Serb, Basel Halak and Themistoklis Prodromakis

Project description

The aim of this PhD studentship is to develop systems exploiting the stochasticity of RRAM devices in order to build securely identifiable systems. Such systems could create hardware whose identity is encoded into atomic-scale device configurations and therefore is practically impossible to forge. We anticipate this to become a key identification strategy for safety and security-critical hardware such as medical and military electronics, therefore, the reliability of the proposed security primitive is vital, in order to ensure that its response is error-free in the presence of temporal and environment variability  (e.g. ageing, temperature fluctuation, etc). This project will address this issue by carrying comprehensive experimental evaluation and will explore the application of emerging low cost reliability enhancement techniques, such as ageing acceleration.

Another important aspect in the proposed design is resilience to security attacks, especially those enhanced by machine learning models, therefore the project will investigate the application of novel countermeasures to achieve the required security level, including , adversarial networks, complexity-based techniques, and obfuscation.  

The project covers a wide spectrum of experimental research, including mixed-signal IC and/or embedded design, machine learning and programming. The PhD student will have the opportunity to join a multi-disciplinary team and to be trained and work in the world-class facilities of the Zepler Institute for Photonics and Nanoelectronics. 

Supervision:

Dr. Alexander Serb – expert in Nanoelectronics.

Dr. Basel Halak – expert in Physical Unclonable Functions and hardware security.

Prof Themistoklis Prodromakis – expert in AI hardware.

This project is funded through the UKRI MINDS Centre for Doctoral Training (www.mindscdt.ai). This is one of 16 PhD training centres in the UK with a unique focus on advancing AI techniques in the context of real-world engineered systems with a remit that spans novel hardware for AI, AI and machine learning, pervasive systems and IoT, and human-AI collaboration. We provide enhanced cross-disciplinary training in electronics and AI, entrepreneurship, responsible research and innovation, communication strategies, outreach and impact development as part of an integrated 4-year iPhD programme. 

The MINDS CDT is based in a dedicated laboratory on Highfield Campus at the University of Southampton. The lab provides a supportive environment for individual research, ideas sharing and collaboration, and the wider campus provides access to substantial high-performance computing (including dedicated GPU servers), maker and cleanroom facilities. You will take part in our annual, student-designed innovation camps, be able to work with industry and government partners through our internship scheme and be able to take part in exchanges with international university partners.

Funding: full tuition for EU/UK Students plus, for UK and EU students resident in the UK for previous 3 years, an enhanced stipend of £18,285, tax-free per annum for 4 years. years. 

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: no later than 5 June 2020 for entry in October 2020. 

How To Apply

Applications should be made online, select the academic session 2020-21 “PhD MINDS” as the programme. Enter Secure computing systems based on stochasticity of RRAM under the Topic or Field of Research.

Applications should include: 

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact: mindscdt@soton.ac.uk 

Closing Date: 05 Jun 2020
Post Type: PhD Studentship (Funded)