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تعداد صفحات این فایل: ۲۶ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:An Efficient Approach for Phishing Detection Using Neuro-Fuzzy Model~~en~~

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.

۱ Introduction

Phishers use a number of techniques to lure their victims, including email messages, instant messages, forum posts, phone calls, and text messages. With these activities of phishing, it causes severe economy loss all over the world. APWG’s second half report for 2010 claimed that phishing attacks grew 142% over the first half of 2010. The report also classifies the targets as comprising 37.9% payment services, 33.1% financial institutions, 6.6% classified, 4.6% gaming, 2.8% social networks, and the remainder in other categories. In 2011, 83% of Americans and 85% of Europeans regularly shopped online (Fortune Magazine, 2011). Meanwhile, phishing sites are also growing rapidly in quality and quantity. Therefore, the risk of stealing user information is extremely high. Because of these reasons, detecting phishing problem is very urgent, complex and extremely important problem in modern society. Recently, there have been many studies which against phishing based on the characteristics of site, such as URL of website, content of website, combining both the website URL and content, source code of website or screenshot of website, Manuscript received September 21, 2014; revised December 11, 2014. 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.

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