Detecting Malicious Facebook Application using Digital India Scheme

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 data set 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 Proguard Facebook's Rigorous Application Evaluator arguably the first tool focused on detecting malicious apps on Facebook. To develop ProGuard, 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 ProGuard can detect malicious apps with 100% accuracy, with no false positives and a low false negative rate (4.1%). 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 1,584 apps enabling the viral propagation of 3,723 other apps through their posts. Long-term, we see ProGuard as a step towards creating an independent watchdog for app assessment and ranking, so as to warn Facebook users before installing apps And improved online infrastructure and by increasing Internet connectivity so that we can avoid fraud and cheating. M. Divya | M. Monika | N. Kanimozhi"Detecting Malicious Facebook Application using Digital India Scheme" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd10964.pdf http://www.ijtsrd.com/engineering/computer-engineering/10964/detecting-malicious-facebook-application-using-digital-india-scheme/m-divya