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首页> 外文期刊>International Journal of Internet Technology and Secured Transactions >Online social network profile data extraction for vulnerability analysis
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Online social network profile data extraction for vulnerability analysis

机译：在线社交网络配置文件数据提取以进行漏洞分析

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The increase in social computing has provided the situation where large amounts of personal information are being posted online. This makes people vulnerable to social engineering attacks because their personal details are readily available. Our automated approach for personal data extraction was developed to extract personal details and top friends from MySpace profiles and place them into a repository. An online social network graph was generated from the repository data where nodes represent peoples' profiles. Analysis was carried out into what factors affect node vulnerability. The graph analysis identified structural features of the nodes, e.g., clustering coefficient, indegree and outdegree, which contribute towards vulnerability. From this, it was found that the number of neighbours and the clustering coefficient were major factors in making a node vulnerable because of the potential to spread personal details around the network. These results provide a good foundation for future work on online vulnerability in online social networks (OSNs).
机译：社交计算的增长提供了在线发布大量个人信息的情况。这使得人们容易受到社会工程攻击，因为他们的个人详细信息随时可用。我们开发了用于提取个人数据的自动方法，以从MySpace档案中提取个人详细信息和主要朋友，并将其放入存储库中。从存储库数据生成了一个在线社交网络图，其中节点代表人们的个人资料。对影响节点漏洞的因素进行了分析。图形分析确定了节点的结构特征，例如聚类系数，进度和出度，这些特征会导致脆弱性。由此发现，邻居的数量和聚类系数是使节点易受攻击的主要因素，因为它可能在网络上散布个人详细信息。这些结果为未来在线社交网络（OSN）中的在线漏洞的工作提供了良好的基础。

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