Phishing identification: An efficient neuro-fuzzy model without using rule sets

The Internet has brought enormous benefits to mankind, but it could be many potential risks. Internet crimes are growing rapidly, phishing is one of the new type of online crime. Phishing site is a fake-site aimed to steal personal information such as password, banking account and credit card information, etc. Most of these phishing pages look similar to the real pages in terms of interface and uniform resource locator (URL) address. Many techniques have been proposed to identify phishing sites. However, the numbers of victims have been increasing due to inefficient protection technique. In this paper, we develop a neuro-fuzzy model for phishing identification efficiently. The model eliminates the subjective factors to improve efficiency such as if-then rule sets, the parameters of membership functions, etc. Moreover, the efficiency features to identify phishing were used for the neuro-fuzzy model. The effectiveness of the proposed technique is examined with large-scale datasets collected from phishing sites and legitimate sites. The results show that the proposed technique can identify over 99% phishing sites.