Real Time Detection System for Malicious URLs

Now a days in context of online social media, hackers have started using social networks like Twitter, Facebook Google+ etc for their unauthorized activities. These are very popular social networking sites which are used by numerous people to get connected with each other and share their every day´s happenings through it. In this paper we consider twitter as such a social networking site to experiment. Twitter is extremely popular for micro-blogging where people post short messages of 140 characters called as tweets. It has over 200 million active users who post approximately 300 million tweets everyday on the walls. Hackers or attackers have started using Twitter as a medium to spread virus as the available information is quite vast and scattered. Also it is very easy to spread and posting URLs on twitter wall. Our experiment shows the detection of Malicious URLs on Twitter in real-time. We test such a method to discover correlated URL redirect chains using the frequently shared URLs. We used the collection of tweets from which we extract features based on URL redirection. Then we find entry points of correlated URLs. Crawler browser marks the suspicious URL. The system shows the expected results of detection of malicious URLs.