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Machine learning attack resistant hardware security for IoT


   School of Electronics, Electrical Engineering and Computer Science

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  Dr Chongyan Gu, Prof M O'Neill  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

According to Cisco, 500 billion devices are expected to be connected to the Internet by 2030. The Covid-19 pandemic, resulting in remote working and home-schooling, is leading to a multiplier effect on rising computing technologies. As devices are connected to the Internet, this opens up a range of new attack vectors for malicious adversaries and hackers.

There has been a significant increase in attacks and threats directed at networks in 2020, including the infamous the internet of things (IoT) botnet attacks, which can harvest confidential data and execute cyber attacks by taking control of the victim’s devices and systems remotely. Additionally, counterfeit devices are an increasing problem as more and more devices are connected online. To address this, this project explores the potential of emerging digital technologies, such as hardware security, machine learning and IoT, to transform the way we design, manufacture and operate products and services. The programme offers a bespoke research and training programme that aims to develop students into cross-disciplinary, industry-conscious thinkers and leaders who will influence the roadmaps of future advanced technologies and their applications. They will have a balanced understanding of ICT (security and data analytics) in the context of their application to advanced technologies and high value designs.

Project Description:

The internet of things (IoT), enabling smart cities and machines widely and intelligently connected, has led to the development of smart factories. The move to IoT devices and machine-to-machine communication poses serious security and privacy issues as there is not direct control over the connected devices. Current cryptographic methods used to secure computers connected to the Internet won’t easily scale to the volume of the IoT, due to both key management issues, as well as the fact that low cost IoT devices often don’t have the computational power required for complex cryptographic computations.

Silicon physical unclonable functions (PUFs), which exploit manufacturing variations of silicon chips, offer a promising mechanism that can be used in many security, protection and digital rights management applications. Such a primitive has a number of desirable properties from a security perspective, such as the ability to provide a low-cost unique identifier for an integrated circuit (IC) or to provide a variability aware circuit that returns a device specific response to an input challenge. This gives it an advantage over current state-of-art alternatives such as secure non-volatile memory (NVM) or trusted platform modules (TPMs). No special manufacturing processes are required to integrate a PUF into a design. This lowers the overall cost of the security for the IC enabling the PUF to be utilised as a hardware root of trust for all security or identity related operations on the device.

However, PUF based authentication/identification schemes are vulnerable to a number of security attacks including machine learning based modelling attacks and physical cloning attacks. The aim of this project is to develop a secure PUF design, which can deliver a corner stone for building unforgeable devices. A low-cost secure PUF-based authentication/identification scheme will lead to a step change in meeting the stringent security requirements of a number of key areas of the digital economy such as Industry 4.0, IoT, and the hardware supply chain.

Project Key Words

Machine learning attacks, hardware security, the internet of things (IoT), authentication

Start Date: 01/10/22

Application Closing date: 28/02/22

For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

Academic Requirements:

A minimum 2.1 honours degree or equivalent in Computer Science or Electrical and Electronic Engineering or relevant degree is required.

Funding Notes:

This three year studentship, for full-time PhD study, is potentially funded by the Department for the Economy (DfE) and commences on 1 October 2022. For UK domiciled students the value of an award includes the cost of approved tuition fees as well as maintenance support (Fees £4,500 pa and Stipend rate £15,609 pa - 2022-23 rates to be confirmed). To be considered eligible for a full DfE studentship award you must have been ordinarily resident in the United Kingdom for the full three year period before the first day of the first academic year of the course.

For candidates who do not meet the above residency requirements, a small number of international studentships may be available from the School. These are expected to be highly competitive, and a selection process will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.

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