Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment

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轻网络攻击，这些攻击越来越多地被用来攻击软件漏洞和弱点。本项目期望构建创新的软件漏洞知识图谱，开发先进的基于图谱的算法和模型。该项目的预期成果包括增强澳大利亚及其他地区的组织和个人抵御网络攻击的能力，图论的理论发展，改进的图神经网络模型和改进的图迁移学习算法。

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.