Detection of Malicious URLs on Twitter

Twitter is an online social network that is popular for its use of 140-character messages called tweets to exchange information, news and connects the global world. Due to the large audience of people that make use of twitter, malicious users from time to time try to find ways to attack it. This is because the usages of URLs in tweets expose them and make them prone to attacks such as malware distribution, phishing, spam and scam. In this project, a system is developed that detected suspicious URLs on twitter, and the proposed system investigates the correlation of URL redirect chains extracted from various tweets on twitter. Therefore, after a large number of tweets are collected from twitter public timeline, a classifier by naive Bayes machine learning algorithm is built using the data.