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:
- 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;
- Develop techniques specifically tailored for the purposes of intelligence collection and analysis on covert networks based on advanced network techniques;
- Test innovative anticipation and disruption techniques at the actor (individual), group, and network levels and across time, informed by network and intelligence methodologies; and
- 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.
The PhD candidate will work on a project aligned with the project outlined above, involving the application of network science (e.g., social network analysis) to examine the structure and dynamics of covert networks (e.g., cybercrime, organised crime, and extremist networks) . Preference will be given to candidates with a background in network science. The PhD can be completed via traditional thesis or by publication, to be agreed with the successful candidate and supervisory team.
Applications close 5pm, Sunday 21 January 2024
This scholarship is available over 3 years.
- Stipend of $33,500 per annum tax exempt (2023 rate)
To be eligible you must:
- be a domestic candidate. The successful candidate is required to be an Australian citizen or permanent resident in accordance with the terms of the funding agreement.
- meet Deakin's PhD entry requirements
- be enrolling full time and hold an honours degree (first class) or an equivalent standard master's degree with a substantial research component.
Please refer to the research degree entry pathways page for further information.
How to apply
Please email a CV and cover letter to Prof Chad Whelan. The CV should highlight your skills, education, publications and relevant work experience. If you are successful you will then be invited to submit a formal application.