November 16, 2023

A PhD scholarship is available to initiate and conduct research on the topic ‘Network science and covert threats

HDR Scholarship – Network science and covert threats

Applications now open. A PhD scholarship is available to initiate and conduct research on the topic ‘Network science and covert threats’.

Network science offers unique concepts, theories, and methods to analyse and understand relationships between a given set of social entitles (individuals, groups, etc.) in ways that directly inform the identification, anticipation, and disruption of covert threats. This project aims to leverage the untapped potential of network science for analysing and disrupting crime and security risks. Developing novel simulation methodologies to systematically analyse the characteristics of cybercrime, organised crime, and extremist networks, the project will use advances in analytics and machine learning to model and reveal effective intelligence targeting and disruption strategies. The project will therefore extend existing research on cover networks in significant ways.

Working in a multi-disciplinary team comprising criminologists, mathematicians, and data scientists, the project principally aims to:

1. Construct a series of empirically informed simulated covert networks across cybercrime, organised crime, and extremism based on a systematic analysis of real-world network data;
2. Develop techniques specifically tailored for the purposes of intelligence collection and analysis on covert networks based on advanced network techniques;
3. Test innovative anticipation and disruption techniques at the actor (individual), group, and network levels and across time, informed by network and intelligence methodologies; and
4. Leverage machine learning to develop algorithms and analytic techniques to automate data collection and analysis, inform target selection and the most effective disruption techniques, and predict the consequences of such interventions.

The project will therefore employ innovative methods to generate empirically informed simulated networks that will compensate for the limitations typical of real-world datasets on covert networks as well as develop and model disruption methodologies based in advanced network research and theory. The project is funded by a National Intelligence and Security Discovery Research Grant.