Learning Software Security Analysers with Imperfect Data

用不完美的数据学习软件安全分析器

• 批准号: FT220100391
• 负责人: A/Prof Yulei Sui
• 金额: $ 62.14万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-06-30 至 2027-06-29
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT220100391
• 关键词: Learning; Software; Security; Analysers; Imperfect

This project aims to systematically investigate next-generation learning-based software security analysis to detect vulnerabilities in real-world large-scale software. The expected learning-based foundation will support the handling of imperfect data in order to provide a precise, scalable and adaptive security analysis of the critical software components, thus capturing important security vulnerabilities missed by existing approaches. The success of this project will further enhance the international competitiveness of Australian research in this important field and will benefit any Australian industry and business where software systems are deeply-rooted, such as transportation, smart homes, medical devices, defence and finance.

该项目旨在系统地研究下一代基于学习的软件安全分析，以检测现实世界大型软件中的漏洞。预期的基于学习的基础将支持处理不完美的数据，以便为关键软件组件提供精确、可扩展和自适应的安全分析，从而捕获现有方法遗漏的重要安全漏洞。该项目的成功将进一步提升澳大利亚在这一重要领域研究的国际竞争力，并将惠及任何软件系统根深蒂固的澳大利亚工业和企业，例如交通、智能家居、医疗器械、国防和金融。