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Insider Threat Detection and Prevention using Artificial Intelligence (AI)


   School of Computing, Engineering & the Built Environment


About the Project

REFERENCE NUMBER

Please select reference number SCEBE/21SF/ITD/Elmrabit on the application selector

BACKGROUND

Insider threat protection has received increasing attention in the last ten years due to the serious consequences of malicious insider threats. Moreover, data leaks and the sale of mass data have become much simpler to achieve, e.g., the dark web can allow malicious insiders to divulge confidential data whilst hiding their identities.

With the use of Artificial Intelligence (AI) [ Machine Learning algorithms (ML) or Deep Learning algorithms (DL) ], researchers could classify insider threat activities and behaviours to detect and prevent insider threat incidents and breaches.

This project aims to propose and develop a novel approach to detect malicious or unintentional insider threats within large organisations.

PROJECT OBJECTIVES

Initial project objectives:

•            Review the current insider threat approaches using AI.

•            Conduct critical research analysis to identify the behaviour of insider threats.

•            Develop the AI tool to detect insider threats.

•            Evaluate the effectiveness of the created tool on various datasets and compare your result with other published results.

SPECIFICATIONS

The successful applicant will be able to demonstrate possession of programming skills but also have good understanding of AI.

Other PhD topics of interest include, but are not limited to:

•            Insider attack modelling and attack vectors.

•            Implications of insider attacks.

•            Policies and regulations to prevent insider attacks.

•            Authentication and authorization techniques to address insider attacks.

•            Behavioral analytics and fraud detection.

•            Data governance and differential privacy to mitigate data leaks.

•            Insider attack recovery mechanisms.

•            Insider attack datasets.

•            Applications of machine learning to detect and prevent insider.

FOR FURTHER INFORMATION

For further information, please contact the project supervisory team below:

Director of Studies

Name: Dr Nebrase Elmrabit

Email:

GCU Research Online URL: https://researchonline.gcu.ac.uk/en/persons/nebrase-elmrabit

Google Scholar URL: https://scholar.google.com/citations?hl=en&user=_DGG5AkAAAAJ

2nd Supervisor

Name: Dr Adnan Akhunzada

Email:

GCU Research Online URL: https://researchonline.gcu.ac.uk/en/persons/adnan-akhunzada

Google Scholar URL: https://scholar.google.com/citations?hl=en&user=9L9UAVcAAAAJ

3rd Supervisor

Name: Dr Jackie Riley

Email:

GCU Research Online URL: https://researchonline.gcu.ac.uk/en/persons/jacqueline-riley


Funding Notes

Applicants are expected to find external funding sources to cover the tuition fees and living expenses. Alumni and International students new to GCU who are self-funding are eligible for fee discounts. See more on fees and funding. View Website

References

For further information, please contact the Director of Studies for this project:
Name: Dr Nebrase Elmrabit
Email: Nebrase.Elmrabit@gcu.ac.uk
Please note, this is an informal query email, and is not considered an application.

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