tailieunhanh - An efficient approach based on neuro fuzzy for phishing detection
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. | Journal of Automation and Control Engineering Vol. 4, No. 2, April 2016 An Efficient Approach Based on Neuro-Fuzzy for Phishing Detection Luong Anh Tuan Nguyen, Huu Khuong Nguyen, and Ba Lam To Ho Chi Minh City University of Transport, Vietnam Email: {nlatuan, nhkhuong}@, tblam83@ 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 using 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—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
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