Abstract—Nowadays, online transactions are becoming more and more popular in modern society. As a result, Phishing is an attempt by an individual or a group of people to steal personal information such as password, banking account and credit card information, etc. Most of these phishing web pages look similar to the real web pages in terms of website interface and uniform resource locator (URL) address. Many techniques have been proposed to detect phishing websites, such as Blacklist-based technique, Heuristic-based technique, etc. However, the numbers of victims have been increasing due to inefficient protection technique. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. This paper proposed a new neuro-fuzzy model without using rule sets for phishing detection. Specifically, the proposed technique calculates the value of heuristics from membership functions. Then, the weights are generated by a neural network. The proposed technique is evaluated with the datasets of 11,660 phishing sites and 10,000 legitimate sites. The results show that the proposed technique can detect over 99% phishing sites.

Index Terms—phishing, neuro-fuzzy, neural network

Cite: Luong Anh Tuan Nguyen, Ba Lam To, and Huu Khuong Nguyen, "An Efficient Approach for Phishing Detection Using Neuro-Fuzzy Model," Jounal of Automation and Control Engineering, Vol. 3, No. 6, pp. 519-525, December, 2015. doi: 10.12720/joace.3.6.519-525