Phishing is one of the prevalent techniques used by attackers to breach security and steal private and confidential information. It has compromised millions of users’ data. Blacklisting websites and heuristic-based methods are common approaches to detect a phishing website. The blacklist method suffers from a window of vulnerability. Many heuristics were proposed in the past. Some of them have better accuracy but a lower performance. A phishing filter should have better accuracy and peformance. It should be able to detect fresh phishing websites. Jo et al.  present a list of attributes of the web page to find the disparity between an original website and a spoofed website. The main aim of this project is to integrate the approach presented by Jo et al.  into web browser via Firefox add-on. Our phishing filter collects the list of attributes and compares it with the help of approximate string matching algorithms and WHOIS  server queries. For machine learning techniques, we used Weka . All the algorithms, available in Weka were applied to our testing data set. Our phishing filter achieves 94.3% accuracy with reasonable performance.
Joshi, Rushikesh, "Interactive Phishing Filter" (2015). Master's Projects. 430.