Abstract—In the Internet era, the online trading of various fields is growing quickly. As a result, cyber crime is increasing constantly. Phishing is a new type of crime aimed at stealing user information via these fake web pages. 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 trained by neural network with adaptive learning rate. 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, adaptive learning rate

Cite: Luong Anh Tuan Nguyen, Huu Khuong Nguyen, and Ba Lam To, "An Efficient Approach Based on Neuro-Fuzzy for Phishing Detection," Jounal of Automation and Control Engineering, Vol. 4, No. 2, pp. 159-165, April, 2016. doi: 10.12720/joace.4.2.159-165