Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment

知识图驱动的软件漏洞风险发现与评估

• 批准号: DP230100716
• 负责人: Dr Jinli Cao
• 金额: $ 31.71万
• 依托单位: La Trobe University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP230100716
• 关键词: Knowledge; Graph; driven; Software; Vulnerability

This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend against cyberattacks for both organisations and individuals in Australia and beyond, theory development in graph theory, refined graph neural network models and improved graph transfer learning algorithms.

该项目旨在通过利用先进的知识图谱和深度学习技术来减少网络攻击，这些攻击越来越多地被用来攻击软件的漏洞和弱点。该项目希望构建一个创新的软件漏洞知识图谱，并开发先进的基于图形的算法和模型。该项目的预期成果包括增强了澳大利亚及其他地区组织和个人抵御网络攻击的能力、图论理论的发展、改进的图形神经网络模型和改进的图形转移学习算法。