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Situated Anomaly Detection in an Open Environment

Project Member(s): Chen, L.

Funding or Partner Organisation: Australian Research Council (ARC Future Fellowships)
Australian Research Council (ARC Future Fellowships)

Start year: 2025

Summary: This project aims to investigate situated anomaly detection in the open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of provided data. In contrast, this project propose to design innovative anomaly detection algorithms by learning from interacting with an open environment, which enables the discovery of emerging new anomalies and the early detection of anomalies. The established theories and developed algorithms will advance frontier technologies in machine intelligence. The success of the project will contribute to a wide range of real applications in cybersecurity, defence, finance and smart homes, bringing massive social and economic benefits.

FOR Codes: Deep learning, Data mining and knowledge discovery, Data engineering and data science, Artificial intelligence, Application software packages