Situated Anomaly Detection in an Open Environment

在开放环境中进行异常检测

• 批准号: FT230100121
• 负责人: Prof Ling Chen
• 金额: $ 78.64万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-06-30 至 2028-06-29
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT230100121
• 关键词: Situated; Anomaly; Detection; Open; Environment

This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of 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 and finance, bringing massive social and economic benefits.

该项目旨在调查在开放环境中进行异常检测。现有的异常检测技术遵循常规机器学习的设置，并从一组收集的数据中发现异常。相比之下，该项目提议通过从与开放环境的相互作用中学习，从而开发出异常检测算法的下一代，从而使新异常和早期检测异常。既定的理论和开发的算法将推进机器智能中的前沿技术。该项目的成功将为网络安全，国防和金融方面的各种真实应用做出贡献，从而带来巨大的社会和经济利益。