A fast and effective automated insider threat detection and prediction system. Threats from insiders directly compromises the security, privacy and integrity of Australian e-commerce, large databases and communication channels. This project will provide an essential step in combating this criminal activity by developing methods to detect such threats and secure the public's information against exposure and identity theft.
Discovery Early Career Researcher Award - Grant ID: DE150101301
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Cognitive Models of Human Decision-making in Cybersecurity Settings. This project aims to study human decision-making by attackers, defenders and users, in a cyber-security setting. Cognitive modelling of these decisions will play a central role in understanding and optimising the safety of cyberspace. This project will involve three components: new behavioural experiments focusing on cybersecurity situations of prevention and detection; cognitive models to understand and predict how people make ....Cognitive Models of Human Decision-making in Cybersecurity Settings. This project aims to study human decision-making by attackers, defenders and users, in a cyber-security setting. Cognitive modelling of these decisions will play a central role in understanding and optimising the safety of cyberspace. This project will involve three components: new behavioural experiments focusing on cybersecurity situations of prevention and detection; cognitive models to understand and predict how people make decisions in such settings; and the evaluation of these models against behavioural data using Bayesian statistical methods. This will then be applied to operational problems that will involve, determining optimal security policies, automated behaviour in adversarial situations, and individualised training.Read moreRead less