Author : Maher Ragheb Mohammed Abur-rous
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (757 download)
Book Synopsis Phishing Website Detection Using Intelligent Data Mining Techniques. Design and Development of an Intelligent Association Classification Mining Fuzzy Based Scheme for Phishing Website Detection with an Emphasis on E-banking by : Maher Ragheb Mohammed Abur-rous
Download or read book Phishing Website Detection Using Intelligent Data Mining Techniques. Design and Development of an Intelligent Association Classification Mining Fuzzy Based Scheme for Phishing Website Detection with an Emphasis on E-banking written by Maher Ragheb Mohammed Abur-rous and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Phishing techniques have not only grown in number, but also in sophistication. Phishers mighthave a lot of approaches and tactics to conduct a well-designed phishing attack. The targets ofthe phishing attacks, which are mainly on-line banking consumers and payment serviceproviders, are facing substantial financial loss and lack of trust in Internet-based services. Inorder to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge andexperience. So far, various solutions have been proposed and developed to address theseproblems. Most of these approaches are not able to make a decision dynamically on whether thesite is in fact phished, giving rise to a large number of false positives. This is mainly due tolimitation of the previously proposed approaches, for example depending only on fixed blackand white listing database, missing of human intelligence and experts, poor scalability and theirtimeliness. In this research we investigated and developed the application of an intelligent fuzzy-basedclassification system for e-banking phishing website detection. The main aim of the proposedsystem is to provide protection to users from phishers deception tricks, giving them the abilityto detect the legitimacy of the websites. The proposed intelligent phishing detection systememployed Fuzzy Logic (FL) model with association classification mining algorithms. Theapproach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamicphishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deceptionbehaviour techniques have been conducted to cover all phishing concerns. A layered fuzzystructure has been constructed for all gathered and extracted phishing website features andpatterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attacktype. To reduce human knowledge intervention, Different classification and associationalgorithms have been implemented to generate fuzzy phishing rules automatically, to beintegrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify allrelevant fuzzy rules from the training data set. A comparative study and analysis showed thatthe proposed learning approach has a higher degree of predictive and detective capability thanexisting models. Experiments also showed significance of some important phishing criteria likeURL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested andvalidated by incorporating the scheme as a web based plug-ins phishing toolbar. The resultsobtained are promising and showed that our intelligent fuzzy based classification detectionsystem can provide an effective help for real-time phishing website detection. The toolbarsuccessfully recognized and detected approximately 92% of the phishing websites selected fromour test data set, avoiding many miss-classified websites and false phishing alarms.