Detecting Web Paged Malicious URL Using Support Vector Machine

Abstract

With 20 million installs a day, third-party apps are a major reason for the popularly 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 compaigns. Our key contribution is in developing FRAppE- Facebook’s  Rigorous applications 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 ex, we find that malicious apps often share names with other apps, and they typically request fewer permissions that 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 1584 apps enabling the virtual propagation of 3723 other apps through their posts.