Fully Funded PhD Studentship: Preventing Romance Fraud

University of Abertay Dundee

United Kingdom

October 29, 2021

Description

Job title

Fully Funded PhD Studentship: Preventing Romance Fraud

Job reference

REQ000713

Date posted

13/09/2021

Application closing date

29/10/2021

Location

Dundee

Salary

Blank

Contractual hours

Blank

Basis

Blank

Job category/type

Graduate School

Attachments

Blank

Job description

Project Title: Broken Hearts, Empty Accounts: Preventing Romance Fraud

PhD Studentship: Abertay University R-LINCS2 funding. A PhD studentship that comprises tax-free stipend of £15,609 (increasing in line with UKRI per annum) per year over 3.5 years, student tuition fees paid, and a generous study package (e.g. limited research consumables, travel budget, and training when appropriate).

The successful candidate will have access to the pan-University Graduate School, which offers facilities and an integrated training programme to the postgraduate community within a single centre, serving to inculcate interdisciplinary and collaborative working in our next generation of researchers.

You will be expected to undertake limited teaching duties of no more than 70 hours a year. We provide training on this activity, and it is a valuable part of career development.

The Studentship is available for a February 2022 start.

Interviews are likely to be online in the November/ December 2021.

Project Description: Romance fraud is a form of social engineering, whereby fraudsters create a fake profile on dating platforms and strike up a relationship with their potential victims, with the end goal of conning individuals out of money. Romance fraud is a growing, particularly cruel form of cybercrime. Victims are left heartbroken, often facing financial ruin. Romance fraud has been exacerbated by the COVID-19 pandemic, alongside other cybercrimes (Lallie et al., 2021). This surge in fraud has presented challenges for law enforcement dealing with this volume of cybercrime (Horgan et al., 2021). Although previous research has been conducted into the detection of fake profiles (Suarez-Tangil et al., 2019), more work is needed to support the decision-making processes of potential victims online.

The PhD project aims to develop a robust machine learning model to detect fraudster behaviour and applies existing theoretical frameworks and decision- making techniques to enable users to recognise fraudsters. Results will impact on society by keeping individuals safe online and informing the work of our project partners in law enforcement.

Supervisory Team: The candidate will be supervised within the Division of Cyber Security by Dr Lynsay Shepherd, Prof Graham Johnson and, Dr Andrea Szymkowiak. For informal enquiries about this studentship please contact Dr Lynsay Shepherd (lynsay.shepherd@abertay.ac.uk).

Entry Requirements: Candidates must have, or must expect to obtain, a first class or upper second-class honours degree in Computing or related disciplines such as Cyber Security, Artificial Intelligence or Computer Games Technology. Consideration will be given to candidates from other disciplines who can demonstrate appropriate research and programming skills.

Candidates must also have:

  • a strong interest in the human aspects of cyber security
  • proficiency with machine learning
  • experience in languages such as Python or Java.
  • knowledge of web technologies such as HTML, JavaScript, and PHP
  • experience of research methods and statistical analysis
  • Applicants who are non-native speakers of English, the University requires IELTS of 6.5 (with no band less than 6.0) or an equivalent qualification accepted by the Home Office.

    Applications and closing date: 29th October 2021

    Applicants should submit through the Abertay University jobs page https: // www. abertay.ac.uk/about/working-at-abertay/jobs/, submitting a personal statement of application detailing why you are interested in undertaking this project, and a CV.

    If you are selected for interview, you will be required to complete an online Research Student Application Form which includes the submission of a research proposal. Guidance on how to write the proposal can be found here: https: // www. abertay.ac.uk/study-apply/how-to-apply/how-to-apply/, Applicants are strongly encouraged to contact Dr. Lynsay Shepherd (lynsay.shepherd@abertay.ac.uk) for advice on developing a proposal prior to submitting it.

    Abertay University was the first university in the world to offer degrees in ethical hacking and computer games. Abertay is currently leading a new £11.7 million project to create a cybersecurity research and development centre as part of the Tay Cities Deal. The University has recently been recognised as an Academic Centre of Excellence in Cyber Security Education (Gold Award) by the National Cyber Security Centre. Abertay is also lead of an £11.5 million R&D Centre based in the heart of the Dundee video games cluster, in partnership with Dundee University and the University of St Andrews.

    Abertay has been named “UK University of the Year for Teaching Quality” by The Times & Sunday Times Good University Guide 2021.

    Entry requirements

    Essential requirements:

  • A first class or upper second-class honours degree in Computing or related disciplines such as Cyber Security, Artificial Intelligence or Computer Games Technology. Consideration will be given to candidates from other disciplines who can demonstrate appropriate research and programming skills.
  • A strong interest in the human aspects of cyber security
  • Proficiency with machine learning
  • Experience in languages such as Python or Java.
  • Knowledge of web technologies such as HTML, JavaScript, and PHP
  • Experience of research methods and statistical analysis
  • Strong writing skills
  • Good interpersonal and communication skills
  • Applicants who are non-native speakers of English, the University requires IELTS of 6.5 (with no band less than 6.5) or an equivalent qualification accepted by the Home Office
  • Desirable requirements (but not essential):

  • Master's qualification in a relevant discipline
  • Knowledge of advanced statistical methods
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