arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1607.04775cs.SI
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在线社交网络中的组织和个人安全

The Security of Organizations and Individuals in Online Social Networks

Aviad Elyashar

摘要:

与在线社交网络(OSNs)相关的严重的隐私和安全问题是本论文的两个补充研究的一部分。在第一项研究中,我们开发了一种通用算法,通过使用Facebook(目前最流行的OSN)和社交机器人来挖掘目标组织的数据。通过与目标组织中的员工建立好友关系,我们的主动社交机器人能够找到新员工和非正式的组织联系,这是我们无法通过被动社交机器人找到的。我们在Facebook OSN上评估了我们的方法,并能够重建三个不同的实际组织中员工的社交网络。此外,在主动社交机器人的爬行过程中,我们发现了多达13.55%的员工和22.27%的非正式组织联系,而不是被动社交机器人执行的爬行过程,没有公司协会作为朋友。在我们的第二项研究中,我们开发了一种通用算法,用于使用组织社交网络的拓扑结构并利用社交机器人来接触特定的OSN用户,这些用户声称自己是目标组织的员工。我们对Facebook上的三个实际组织和Xing OSN(另一个流行的OSN平台)上的两个实际组织的目标用户进行了评估。最终,我们的社交机器人能够接触到特定的用户,在Facebook上的成功率高达70%,在Xing上高达60%。

英文摘要:

The serious privacy and security problems related to online social networks (OSNs) are what fueled two complementary studies as part of this thesis. In the first study, we developed a general algorithm for the mining of data of targeted organizations by using Facebook (currently the most popular OSN) and socialbots. By friending employees in a targeted organization, our active socialbots were able to find new employees and informal organizational links that we could not find by crawling with passive socialbots. We evaluated our method on the Facebook OSN and were able to reconstruct the social networks of employees in three distinct, actual organizations. Furthermore, in the crawling process with our active socialbots we discovered up to 13.55% more employees and 22.27% more informal organizational links in contrast to the crawling process that was performed by passive socialbots with no company associations as friends. In our second study, we developed a general algorithm for reaching specific OSN users who declared themselves to be employees of targeted organizations, using the topologies of organizational social networks and utilizing socialbots. We evaluated the proposed method on targeted users from three actual organizations on Facebook, and two actual organizations on the Xing OSN (another popular OSN platform). Eventually, our socialbots were able to reach specific users with a success rate of up to 70% on Facebook, and up to 60% on Xing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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