Online spam identification in twitter using naive bayes classifier

Master of Science in Computer Science and Information security 2013-2015 INT Sabu Thampi

Social networking has become a popular way for users to meet and interact online. Users spend a significant 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 thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.
Spam has serious negative on the usability of email and network resources. Spam is flooding the internet with many copies of the same message, in an attempt to force the message, on people who would not otherwise choose to receive it. Negative effect of spam is 419 Scam and phishing are example of cyber crime. Because of IPv6 Spammers get freedom to send unsolicited bulk mail to millions of users. And despite the evolution of anti spam software, such as spam filters and spam blockers, the negative effects of spam are still being felt by individuals and businesses alike. . To prevent this advance techniques are necessary. My software divides messages in spam class and non spam class according to different attribute values of spam.