Phishing detection plug-in toolbar using intelligent Fuzzy-classification mining techniques

Abstract

Detecting phishing website is a complex task which requires significant expert knowledge and experience. So far, various solutions have been proposed and developed to address these problems. Most of these approaches are not able to make a decision dynamically on whether the site is in fact phished, giving rise to a large number of false positives. In this paper we have investigated and developed the application of an open source intelligent fuzzy-based classification system for e-banking phishing website detection. The main goal of the proposed system is to provide protection to users from phishers deception schemes, giving them the ability to detect the legitimacy of the websites. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with classification mining algorithms. The approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic phishing features, with the capability to classify the phishing fuzzy rules. The proposed intelligent phishing website detection system was developed, tested and validated by incorporating the scheme as a web based plug-in phishing toolbar. The results obtained are promising and showed that our intelligent fuzzy based classification detection system can provide an effective help for real-time phishing website detection. The toolbar successfully recognized and detected approximately 86% of the phishing websites selected from our test data set, avoiding many miss-classified websites and false phishing alarms.