AI-based biometric sentimental analysis protocols for ATM fraud detection

Edinburgh Napier University

ATM and Cash machine frauds are on the rise and people are mugged or their sensitive information is used to draw cash or extract banking details. This project is to look into various sentimental analysis approaches while using the cash machine’s built-in camera. The project is going to look into various theft incidents that happen and look into various sentimental analysis techniques used to detect facial gestures and emotions. An AI-based sentimental analysis framework is going to be developed to detect possible fraud during cash machine use. 

Academic qualifications 

A first degree (at least a 2.1) ideally in Computer Science or Cyber Security with a good fundamental knowledge of Cryptography and Machine Learning.

English language requirement 

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online. 

Essential attributes: 

  • Experience of fundamental aspects of computer science, cryptography and machine learning
  • Competence in mathematical computations and authentication protocols
  • Knowledge of machine learning and deep learning
  • Good written and oral communication skills  
  • Strong motivation, with evidence of independent research skills relevant to the project 
  • Good time management 

Desirable attributes:  

  • Knowledge and understanding of different computational algorithms and functions
  • Knowledge and understanding of using tools, such as ProVerif, Tamarin Prover and Scyther
  • Knowledge and understanding of using simulation tools and techniques, such as Matlab.

For enquiries about the content of the project, please email Dr Zeeshan Siddiqui at

For information about how to apply, please visit our website

To apply, please select the link for the PhD Computing FT application form

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