Phishing is a type of social engineering attack which helps to used to steal user data, including login credentials. Finding any Phishing website is really a complex plus dynamic problem involving numerous factors & criteria. Traditional we have anti-phishing tools such as such as Phish net, lexical-based on-line learning, and a proactive phishing identification approach. However, traditional approaches did not mitigate the phishing attacks in the social websites particularly in URLs. In this paper we are implementing machine learning algorithms to detect and mitigate the phishing attacks. We propose a learning based approach to classifying Web sites into 3 categories such as Benign, Spam and Malicious. Our mechanism only analyses the Uniform Resource Locator (URL) itself without accessing the content of Web sites. Thus, it eliminates the run-time latency and the possibility of exposing users to the browser based vulnerabilities.
Volume 11 | 03-Special Issue
Pages: 1370-1373