Detecting E Banking Phishing Using Associative Classification
There are number of users who purchase products online and make payment through e- banking. There are e- banking websites who ask user to provide sensitive data such as username, password or credit card details etc often for malicious reasons. This type of e-banking websites is known as phishing website. In order to detect and predict e-banking phishing website. We proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm. We implemented classification algorithm and techniques to extract the phishing data sets criteria to classify their legitimacy. The e-banking phishing website can be detected based on some important characteristics like URL and Domain Identity, and security and encryption criteria in the final phishing detection rate. Once user makes transaction through online when he makes payment through e-banking website our system will use data mining algorithm to detect whether the e-banking website is phishing website or not. This application can be used by many E-commerce enterprises in order to make the whole transaction process secure. Data mining algorithm used in this system provides better performance as compared to other traditional classifications algorithms. With the help of this system user can also purchase products online without any hesitation.
|IEEE Paper Year||2015|