Damien McGeer

  ne22153@bristol.ac.uk 

Year 2 Student - 2025 Cohort - Cohort 7

I recently graduated from a BSc in Computer Science at the University of Bristol, with the majority of my final year being focused on Human-Computer Interaction and Security Behaviours. My dissertation focused on using quantitative analysis methods to assess the characteristics of rhythm in video games, and the relations between rhythm and player experience. My general research interests lie in the overlap between HCI and Cybersecurity (e.g. Usable security and Cyberpsychology), though I'm keeping my options open while I'm in my first year. 

 

PhD Abstract 

Examining the Degradation of Secure Decision Making in Actor-Driven Insider Threats

Actor-driven insider threats are those who are the subject of social engineering and/or recruitment efforts from a malicious outside party, an aspect of the insider threat problem which is under-researched despite its atypical nature. Current research posits that actor-driven behaviour differs from expected threat activity, but the latent factors and theories at play have yet to be explored in depth. To address this problem, this project aims to examine the development of risk-taking behaviour and perception over the lifetime of an attack - how it may vary and the factors which could contribute to this change.

Events Attended 

South West UK Pre-CHI 2026
RISCS Annual Conference 2026

Academic and Industry Placements completed - Year 1

Academic placement: "Reviewing Behavioural Analytic Metrics for Insider Threat Detection"

I spent 2 weeks researching commonly used databases for insider threat detection systems within research, and extracted the metrics they used to train models and identify anomalies. Most of this time was spent on the CERT Insider and TWOS datasets, weighing up the tradeoff between synthetic data and availability.

Industry placement: "Friendly Friction in e-Learning Design" with RedFlags

I spent 2 weeks researching methods and theories which underpin user engagement and desirable difficulty in the context of e-learning systems.