Defending Machine Learning Operations (MLOps) across the Human-Machine Interface

跨人机界面保护机器学习操作 (MLOps)

• 批准号: NI210100139
• 负责人: Prof Debi Ashenden
• 金额: $ 36.17万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: National Intelligence and Security Discovery Research Grants
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-12-30 至 2024-12-29
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/NI210100139
• 关键词: Defending; Machine; Learning; Operations; MLOps

This project aims to address the socio-technical cybersecurity risks of operationalising machine learning models. It will generate new knowledge in the areas of computer security and human-computer interaction by using a transdisicplinary research approach that brings together social and behavioural science, computer science, and data science. The outputs from the research will be models of the behavioural risks to machine learning operations; a tool for facilitating experiments to manage the risks of human-machine teaming and novel algorithms that can be used to both defend and attack machine learning operations. The benefits arising from the research with be increased trust in the operationalisation of machine learning models.

该项目旨在解决操作机器学习模型的社会技术网络安全风险。它将通过使用跨学科的研究方法，将社会和行为科学，计算机科学和数据科学结合起来，在计算机安全和人机交互领域产生新的知识。该研究的成果将是机器学习操作的行为风险模型;一种促进实验以管理人机合作风险的工具，以及可用于防御和攻击机器学习操作的新算法。研究带来的好处是增加了对机器学习模型操作的信任。