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Secure and Robust Incremental Deep Learning PUF based Authentication Mechanism for IoT Devices (Advert Reference: RDF22-R/EE/CIS/SIDDIQUI)

   Faculty of Engineering and Environment

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  Dr Zeeshan Siddiqui  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

The aim of this project is to explore the existing authentication vulnerabilities within the IoT network and devices using PUF-based authentication and propose a secure and robust incremental deep learning PUF-based authentication mechanism for IoT network or devices. The project is divided into phases. 


Covers a detailed and discreet level of literature review and further analysis of various PUF-based authentication frameworks for IoT devices. Further, it reviews various PUF-based authentication protocols that securely integrates IoT devices/network with deep learning authentication. 


This phase involves proposing a secure and robust PUF-based authentication framework using the incremental deep learning method, such as Contauth and incremental Support Vector Machine (SVM). The framework consists of several authentication phases ensuring confidentiality, integrity and availability. 


This phase discusses the implementation and simulative testing of the designed and developed authentication protocols using authentication protocols validation and verification tools, such as Tamarin Prover, Scyther or ProVerif. The automated security analysis will be followed by logical security analysis using BAN or SVO logic.


This phase is going to discuss the Performance analysis of the implemented authentication framework and protocols along with analysing and comparing with the existing studies. This phase further utilize various multicriteria decision analysis models to logically compare the security and performance of the proposed study with the existing and previous studies followed by a detailed critical analysis. 

The Principal Supervisor for this project is Dr. Zeeshan Siddiqui.

Eligibility and How to Apply:

Please note eligibility requirement:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere or if they have previously been awarded a PhD.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF22-R/…) will not be considered.

Deadline for applications: 20 June 2022

Start Date: 1 October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.

Funding Notes:

Each studentship supports a full stipend, paid for three years at RCUK rates (for 2022/23 full-time study this is £16,602 per year) and full tuition fees. Only UK candidates may apply.

Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,961 per year and full tuition fees) in combination with work or personal responsibilities.

Please note: to be classed as a Home student, candidates must meet the following criteria:

• Be a UK National (meeting residency requirements), or

• have settled status, or

• have pre-settled status (meeting residency requirements), or

• have indefinite leave to remain or enter.


Zeeshan Siddiqui, Jiechao Gao and Nida Z, “An Improved and Secure PUF-PKI Authentication Scheme for Internet of Things (IoT)”, accepted to be published in, IEEE Internet of Things (IoT). (2021)

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