Phishing Website Detection using Intelligent Data Mining Techniques

Maher Aburrous

ISBN 10: 3847335294 / ISBN 13: 9783847335290

Edité par LAP LAMBERT Academic Publishing Jan 2012, 2012

A propos de cet article

Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch.

N° de réf. du vendeur 9783847335290

Détails bibliographiques

• Titre: Phishing Website Detection using Intelligent Data Mining Techniques
• Éditeur: LAP LAMBERT Academic Publishing Jan 2012
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3847335294
• ISBN à 13 chiffres: 9783847335290
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.