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. | Journal of Automation and Control Engineering Vol. 3, No. 6, December 2015 An Efficient Approach for Phishing Detection Using Neuro-Fuzzy Model Luong Anh Tuan Nguyen, Ba Lam To, and Huu Khuong Nguyen Ho Chi Minh City University of Transport, Vietnam Email: {nlatuan, nhkhuong}@, tblam83@ etc. However, each of study has its own strengths and weaknesses. There is still not a sufficient method. In this paper, a new approach is proposed to detect the phishing sites that focuses on the features of URL (PrimaryDomain, SubDomain, PathDomain) and the ranking of site (PageRank, AlexaRank, AlexaReputation). Then, a proposed neuro-fuzzy network is a system which reduces the error and increases the performance. The proposed neuro-fuzzy model uses computational models to perform without rule sets. The proposed solution achieved detection accuracy above 99% with low false signals. The rest of this paper is organized as follows: Section II presents the related works. System design is shown in section III. Section IV evaluates the accuracy of the method. Finally, Section V concludes the paper and figures out the future works. 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. .
