Paper Title
A Review Of Detection Of Malicious Urls In Twitter Stream Using A Near Real Time System

Abstract— This paper presents a survey of twitter stream which is vital for finding twitter attacks. Online social networking has become a very popular way for users to meet and interact online. Users spend a large amount of time on popular social network platforms such as Facebook, MySpace, or Twitter, storing and sharing a wealth of personal information. This information, as well as the possibility of contacting billions of users, also attracts the interest of cybercriminals. Millions of users tweeting around the world, real time systems and different types of mining tools are emerging to allow people tracking the events and post on Twitter. Twitter allows users to discuss events and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about trending topics on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL that lead users to completely unrelated websites. This kind of spam can contribute to devalue real time search services unless mechanisms to fight and stop spammers can be found. Online social networks are extremely popular among Internet users. Unfortunately, in the wrong hands they are also effective tools for executing spam campaigns. So to avoid that presenting an online spam filtering system that can be deployed as a component of the online social networks platform to inspect messages generated by users in real-time. And that reconstructs spam messages into campaigns for classification rather than determine them individually. Although campaign identification has been used for offline spam filtering, apply this technique to aid the online spam detection problem with low overhead. Accordingly, system adopts a set of features that effectively distinguish and determine spam campaigns. It drops messages classified as “spam” before they reach the intended destination, thus protecting them from various kinds of malicious aspects. Firstly collecting a large dataset of Twitter. From that construct a large labeled collection of users, manually classified into spammers and non- spammers. Then identify a number of characteristics related to tweet content and user social behavior, which could potentially be used to detect spammers. Used these characteristics as attributes of machine learning process for classifying users as either spammers or nonspammers. This strategy succeeds at detecting much of the spammers while only a small percentage of non-spammers are misclassified. In this system investigates correlations of URL redirect chains extracted from several tweets and forms frequently shared url. Because attackers have limited resources and usually they reuse them. Develop methods to discover correlated URL redirect chains using the frequently shared URLs and to determine their suspiciousness. Collect numerous tweets from the Twitter public timeline and build a statistical classifier using them. Evaluation results show that classifier accurately and efficiently detects suspicious URLs.