Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach
Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach
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DOI：
10.1109/isi49825.2020.9280544
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Ben Lazarine;Sagar Samtani;Mark W. Patton;Hongyi Zhu;Steven Ullman;Benjamin Ampel;Hsinchun Chen
中科院分区：
文献类型：
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作者：
Ben Lazarine;Sagar Samtani;Mark W. Patton;Hongyi Zhu;Steven Ullman;Benjamin Ampel;Hsinchun Chen
科学网络基础设施社区严重依赖基于公共互联网的系统（例如 GitHub）来共享资源和协作。 GitHub 是最强大和最受欢迎的开源协作系统之一，它允许用户在公共空间中共享和处理项目，以加速开发和部署。监控 GitHub 是否存在暴露的漏洞可以节省财务成本并防止网络基础设施的滥用和攻击。可以利用可以直接与 GitHub 连接的漏洞扫描器来进行此类监控。这项研究旨在主动识别科学网络基础设施中的弱势群体。我们使用社交网络分析来构建表示用户和存储库之间关系的图表。我们利用流行的无监督图嵌入算法来生成图嵌入，以捕获我们的存储库和用户图的网络属性和节点特征。这使得具有相似网络属性和漏洞的公共网络基础设施存储库和用户能够聚集。这项研究的结果发现，主要的科学网络基础设施存在与秘密泄露和高影响力基因组学研究的不安全编码实践相关的漏洞。这些结果可以帮助组织有针对性地解决易受攻击的存储库和用户的问题。
The scientific cyberinfrastructure community heavily relies on public internet-based systems (e.g., GitHub) to share resources and collaborate. GitHub is one of the most powerful and popular systems for open source collaboration that allows users to share and work on projects in a public space for accelerated development and deployment. Monitoring GitHub for exposed vulnerabilities can save financial cost and prevent misuse and attacks of cyberinfrastructure. Vulnerability scanners that can interface with GitHub directly can be leveraged to conduct such monitoring. This research aims to proactively identify vulnerable communities within scientific cyberinfrastructure. We use social network analysis to construct graphs representing the relationships amongst users and repositories. We leverage prevailing unsupervised graph embedding algorithms to generate graph embeddings that capture the network attributes and nodal features of our repository and user graphs. This enables the clustering of public cyberinfrastructure repositories and users that have similar network attributes and vulnerabilities. Results of this research find that major scientific cyberinfrastructures have vulnerabilities pertaining to secret leakage and insecure coding practices for high-impact genomics research. These results can help organizations address their vulnerable repositories and users in a targeted manner.