iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow
iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow
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DOI：
10.24963/ijcai.2019/315
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Yujie Fan;Yiming Zhang;Shifu Hou;Lingwei Chen;Yanfang Ye;C. Shi;Liang Zhao;Shouhuai Xu
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文献类型：
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作者：
Yujie Fan;Yiming Zhang;Shifu Hou;Lingwei Chen;Yanfang Ye;C. Shi;Liang Zhao;Shouhuai Xu
随着GitHub和Stack Overflow等现代社交编码平台越来越受欢迎，其潜在的安全风险也在增加（例如，危险的或恶意的代码可以被容易地嵌入和分发）。为了增强社交编码的安全性，在本文中，我们提出在GitHub和Stack Overflow之间自动化跨平台用户识别，以打击试图毒害现代软件编程生态系统的攻击者。为了解决这个问题，这项工作带来的一个重要见解是利用社交编码属性以及用户属性进行跨平台用户识别。为了描述GitHub和Stack Overflow中的用户（附加属性信息）、项目、问题和答案以及它们之间丰富的语义关系，我们首先引入一个属性异构信息网络（AHIN）进行建模。然后，我们提出了一种新的AHIN表示学习模型AHIN2Vec来有效地学习节点（即，用户）表示，用于跨平台的用户识别。对GitHub和Stack Overflow的数据收集进行了全面的实验，通过与其他基线的比较，验证了我们开发的系统iDev集成我们提出的方法在跨平台用户识别中的有效性。
As modern social coding platforms such as GitHub and Stack Overflow become increasingly popular, their potential security risks increase as well (e.g., risky or malicious codes could be easily embedded and distributed). To enhance the social coding security, in this paper, we propose to automate cross-platform user identification between GitHub and Stack Overflow to combat the attackers who attempt to poison the modern software programming ecosystem. To solve this problem, an important insight brought by this work is to leverage social coding properties in addition to user attributes for cross-platform user identification. To depict users in GitHub and Stack Overflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we first introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to efficiently learn node (i.e., user) representations in AHIN for cross-platform user identification. Comprehensive experiments on the data collections from GitHub and Stack Overflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identification by comparisons with other baselines.