Phishing Website Detection using Intelligent Data Mining Techniques: Design and Development of an Intelligent Association Classification Fuzzy Based Scheme for Phishing Website Detection

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, giving rise to a large number of false positives. This is mainly due to limitation of the previously proposed approaches. In this book, we investigate and develop the application of an intelligent fuzzy-based classification system for phishing website detection. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with association classification mining algorithms. Different phishing experiments which cover all phishing attacks, motivations and deception behavior techniques have been conducted to cover all phishing concerns. A comparative study and analysis showed that the proposed learning approach has a higher degree of predictive and detective capability than existing models. The proposed system was developed, tested and validated by incorporating the scheme as a web based plug-ins phishing toolbar to provide an effective help for real-time phishing website detection for all internet users.

Dr. Maher Aburrous is an Assistant professor at Faculty of Science and Information Technology- Zarak University since 2010. He received his BS.c in Computer Science from Kuwait University in 1989. M.Sc in CIS from Arab Academy in 2003. Aburrous received his M.Phil. and Ph.D. degree from University of Bradford in 2007 and 2010 respectively in UK.