Published Paper Details:

Twitter is a social network and it can be used by more number of peoples. While open the normal browser many number of pages are opened .In that, number of suspicious pages and malicious pages are occur. It detects the unwanted pages based on the redirect chain and correlation of redirection chain features are extracted from the suspicious URL.Twitter is prone to malicious tweets containing URLs for spam, phishing, and malware distribution. Twitter spam detection schemes utilize account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. These detection schemes are ineffective against feature fabrications. Since it consume much time and more resources. Conventional suspicious URL detection schemes utilize several features including lexical features of URLs, URL redirection, HTML content, and dynamic behavior. This system investigates correlations of URL redirect chains extracted from several tweets. Because attackers have limited resources and usually reuse them, their URL redirect chains frequently share the same URLs. It develop methods to discover correlated URL redirect chains using the frequently shared URLs and to determine their speciousness. Evaluation results show that the classifier accurately and efficiently detects suspicious URLs and it provides high security against suspicious uniform resource location in social networks.