UNSW has significant experience supporting and delivering IW and information-related innovation, science and technology activities. Recent examples include co-leading an IW research collaboration with DST, IWD, the University of Adelaide, the University of Melbourne, Edith Cowan University and Macquarie University (MyIP 10379); support to multiple Potentium exercises at UNSW Canberra’s cyber labs; development of patterns of life algorithms for SEA5011 (MyIP 10282); and the conduct of many information and IW-related research projects for and with DST Group, other Defence services and groups, ARC, Cyber Security CRC and other sponsors. Further information can be provided on request.
Information Warfare and Influence operations involve using information to deliberately confuse, manipulate, mislead, and influence choices and decisions and preventing the adversary from doing so. Information influence and warfare are not new however cyberspace provides an arena to influence rapidly and at scale.
Cyberspace has become the ideal platform for the conduct of clandestine intelligence collection, reconnaissance, and influence operations. Militaries, organisations, and governments need to understand these emerging environments. Our researchers focus on understanding modern conflict through gamification, simulation, and wargaming. We study influence propagation, wargaming, and cyber threats and how to identify and disrupt dis/misinformation and propaganda.
Modelling, simulation and visualisation Multidisciplinary research capabilities in IW topics including: psychology; linguistics; foreign policy research; political influence campaigns; military information operations; detecting and countering persuasive, influential and deceptive communication including scams in online environments; detecting and countering accidental and malicious insider threats; microelectronics and nanosensors; intelligent reflective (or reconfigurable) surfaces; auto-generation of security keys from observation of digital identities; modelling techniques in complex multi-agent emergent systems to predict the effects of grey-zone activities; human-device interactions (see National Facility for Human-Robot Interaction Research); viewer perceptions of graphical information elements; remote sensing of human activities through radio signals; narrative design and narrative mapping; social media network analysis; integration of information objectives with strategic and operational campaigns; and information processing and narrative sensemaking in immersive and simulated environments.
Modelling, simulation and visualisation capabilities of relevance to IW including: data science research and education capabilities that enable the creation, development and deployment of data-driven decision-making tools (see UNSW Data Science Hub); advanced imaging technologies enabling 3D data visualisation (see UNSW EPICentre); and 3D geospatial modelling.
Artificial intelligence, machine learning and related capabilities in IW topics including: architectures for Human-AI teaming in information spaces; AI models for influence and shaping interactions; AI-enabled agile-anti-fragile C2 for joint operations; modelling and simulation in heterogeneous information spaces; machine learning for explainable causal inferencing in information spaces especially with complex influence and shaping interactions; counter-AI for deception, exploitation and statistical learning, machine learning algorithms and big data analytics for developing new methods and techniques that protect systems and networks against complex cyberattacks. UNSW is currently working to establish an Institute uniting academics with expertise in data science, AI and ML. A Defence-focussed hub of the Institute will be established at UNSW Canberra at ADFA.
Associate Professor Stephen Doherty
Language studies, Cyber security and privacy, Psycholinguistics
Associate Professor Rob Nicholls
Commercial and contract law, Telecommunications regulation
Associate Professor Benjamin Turnbull
Cybersecurity simulation, Influence propagation, Machine learning