Data mining a way to solve Phishing Attacks

With the ever increasing use of Internet by different stake holders in various fields, information on web browsers and servers is highly susceptible to different security attacks. Though high security measures and enhanced techniques are used to protect the information on the web browsers and servers, they are still prone to a number of attacks. Phishing is one such type of attack in which users are tricked by the phishers using social engineering methods to steal their personal or confidential information. Detection of phishing attack with high accuracy is a challenging research issue. Users are duped by the phishers to enter their confidential information into websites created by them and thereby are steal the vital user's credentials. Phishing sites are normally detected by using blacklist based approach but this approach fails as white listed phishing sites cannot be detected using this approach. This research work aims to use data mining algorithms to analyze E-mails and also helps in preventing phishing attacks. This paper proposed an architectural model to differentiate between the fake E-mail and real E-mail with a high accuracy and use naive Bayesian classification for the said purpose. The proposed algorithm works in various stages for fake E-mail detection and hence tries to protect the users from leaking their confidential information.