Making Facebook More Secure

Abstract

With 20 million installs a day, third-party apps are a primary motive for the popularity and addictiveness of facebook. Unluckily, hackers have realized the skills of utilizing apps for spreading malware and spam. The crisis is already big, as we discover that at the least 13% of apps in our dataset are malicious.  So far, the study group has enthusiastic about detecting malicious posts and campaigns. In this paper, we're going to find that applications are malicious or not. We use expertise collected through observing the posting behavior of common facebook apps which can be going for walks on it. So, first we try to find out the points of malicious apps and different traits of malicious apps which are unsafe to users. In this task, we came up with a framework with which automated detection of false profiles is feasible and is effective. Extra framework makes use of classification methods like support Vector machine, Naive Bayes and selection trees to classify the profiles into fake or actual ones. As, that is an automated detection process, it may be applied without difficulty by means of online social networks which has millions of profiles whose profiles cannot be examined manually.