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REPLOT: REtrieving Profile Links On Twitter for malicious campaign discovery

机译：REPLOT：在Twitter上检索个人资料链接以发现恶意活动

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摘要

Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns.
机译：社交网站越来越容易受到恶意活动的攻击，例如自我传播的蠕虫，信任骗局和按下载下载的恶意软件。与敏感数据（例如个人或专业信息）的存在相关联的大量用户对于攻击者而言无疑是前所未有的机会。这些攻击者正在从以前的攻击平台（如电子邮件）转向社交网站。在本文中，我们提出了一种用于识别社交网站上的恶意个人资料活动的全栈方法，该方法由恶意分类，活动发现和攻击分析组成。用于在Twitter上检索配置文件链接的名为REPLOT的方法包括三个主要阶段。首先，分析配置文件以确定它们更可能是恶意的还是良性的。其次，使用后期数据融合方法检索可疑恶意配置文件之间的联系，该方法包括基于时间和作者身份分析的模型以发现活动。第三，对发现的战役进行分析以调查攻击。在本文中，我们将这种方法应用于现实世界的数据集，以期了解恶意配置文件，其攻击方法及其连接之间的联系。我们的分析确定了一组链接的配置文件，这些配置文件侧重于传播恶意链接以及对攻击活动的另外两个主要类别进行概要分析。

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