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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 28,98 für den Versand von Vereinigtes Königreich nach USA
Versandziele, Kosten & DauerEUR 23,00 für den Versand von Deutschland nach USA
Versandziele, Kosten & DauerAnbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 192 pp. Englisch. Bestandsnummer des Verkäufers 9783847335290
Anzahl: 2 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aburrous MaherDr. 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. Bestandsnummer des Verkäufers 5510839
Anzahl: Mehr als 20 verfügbar
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. 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. Bestandsnummer des Verkäufers 9783847335290
Anzahl: 1 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783847335290
Anzahl: 1 verfügbar
Anbieter: Mispah books, Redhill, SURRE, Vereinigtes Königreich
Paperback. Zustand: Like New. Like New. book. Bestandsnummer des Verkäufers ERICA79638473352946
Anzahl: 1 verfügbar