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Experimenting with anomaly detection features for detecting insider attacks

Supervisors

Sadie Creese
(World Economic Forum Cyber Security Centre, Strategic Advisory Board Member World Economic Forum Cyber Security Centre, Strategic Advisory Board Member)

Suitable for

MSc in Computer Science
Computer Science, Part B
Mathematics and Computer Science, Part C
Computer Science and Philosophy, Part C
Computer Science, Part C

Abstract

This project will use an anomaly detection platform being developed by the Cyber Security Analytics Group to consider relative detection performance using different feature sets, and different anomalies of interest, in the face of varying attacks. This research would be experimental in nature and conducted with a view to exploring the minimal sets that would result in detection of a particular threat. Further reflection would then be given to how generalisable this result might be and what we might determine about the critical datasets required for this kind of control to be effective.