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Detecting Malicious Facebook Applications

机译:检测恶意Facebook应用程序

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With 20 million installs a day , third-party apps are a major reason for the popularity and addictiveness of Facebook. Unfortunately, hackers have realized the potential of using apps for spreading malware and spam. The problem is already significant, as we find that at least 13% of apps in our dataset are malicious. So far, the research community has focused on detecting malicious posts and campaigns. In this paper, we ask the question: Given a Facebook application, can we determine if it is malicious? Our key contribution is in developing FRAppE—Facebook’s Rigorous Application Evaluator—arguably the first tool focused on detecting malicious apps on Facebook. To develop FRAppE, we use information gathered by observing the posting behavior of 111K Facebook apps seen across 2.2 million users on Facebook. First, we identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging these distinguishing features, we show that FRAppE can detect malicious apps with 99.5% accuracy, with no false positives and a high true positive rate (95.9%). Finally, we explore the ecosystem of malicious Facebook apps and identify mechanisms that these apps use to propagate. Interestingly, we find that many apps collude and support each other; in our dataset, we find 1584 apps enabling the viral propagation of 3723 other apps through their posts. Long term, we see FRAppE as a step toward creating an independent watchdog for app assessment and ranking, so as to warn Facebook users before installing apps.
机译:每天有2000万次安装,第三方应用程序是Facebook受欢迎和令人上瘾的主要原因。不幸的是,黑客已经意识到使用应用程序传播恶意软件和垃圾邮件的潜力。这个问题已经很严重,因为我们发现数据集中至少有13%的应用程序是恶意的。到目前为止,研究社区已将重点放在检测恶意帖子和活动上。在本文中,我们提出一个问题:给定一个Facebook应用程序,我们可以确定它是否为恶意软件?我们的主要贡献是开发了FRAppE(Facebook的严格应用评估程序),可以说是第一个专注于检测Facebook上恶意应用程序的工具。为了开发FRAppE,我们使用了通过观察Facebook上220万用户看到的111K Facebook应用程序的发布行为而收集的信息。首先,我们确定了一组功能,可帮助我们区分恶意应用程序与良性应用程序。例如,我们发现恶意应用程序经常与其他应用程序共享名称,并且与良性应用程序相比,它们通常请求更少的权限。其次,利用这些独特的功能,我们表明FRAppE可以检测到具有99.5%准确性的恶意应用,没有误报和很高的真实率(95.9%)。最后,我们探索了恶意Facebook应用程序的生态系统,并确定了这些应用程序用于传播的机制。有趣的是,我们发现许多应用程序相互配合并相互支持。在我们的数据集中,我们发现有1584个应用程序通过其帖子实现了3723个其他应用程序的病毒传播。从长远来看,我们认为FRAppE是朝着建立独立的看门狗以评估和排名应用程序迈出的一步,以便在安装应用程序之前警告Facebook用户。

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