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.
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