فایل ورد کامل شناسایی فیشینگ: یک مدل عصبی فازی کارآمد بدون استفاده از مجموعه قوانین

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تعداد صفحات این فایل: ۲۲ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Phishing Identification: An Efficient Neuro-Fuzzy Model Without Using Rule Sets~~en~~

Abstract

The Internet has brought enormous benefits to mankind, but it could be many potential risks. Internet crimes are growing rapidly, phishing is one of the new type of online crime. Phishing site is a fake-site aimed to steal personal information such as password, banking account and credit card information, etc. Most of these phishing pages look similar to the real pages in terms of interface and uniform resource locator (URL) address. Many techniques have been proposed to identify phishing sites. However, the numbers of victims have been increasing due to inefficient protection technique. In this paper, we develop a neuro-fuzzy model for phishing identification efficiently. The model eliminates the subjective factors to improve efficiency such as if-then rule sets, the parameters of membership functions, etc. Moreover, the efficiency features to identify phishing were used for the neuro-fuzzy model. The effectiveness of the proposed technique is examined with large-scale datasets collected from phishing sites and legitimate sites. The results show that the proposed technique can identify over 99% phishing sites.

۱ Introduction

Phisher use a number of techniques to fool their victims aimed to steal personal information including email messages, instant messages, forum post, phone calls and social networking. These activities of phishing cause severe economy loss all over the world. According to Fortune Magazine, in 2011, 83% of Americans and 85% of Europeans regularly shopped online. Meanwhile, phishing sites are also growing rapidly in quality and quantity. Therefore, the risk of stealing user information is extremely high. For of these reasons, identifying phishing problem is very urgent, complex and extremely important problem.

In this paper, an efficient method is proposed to identify the phishing sites that focuses on the features of URL (PrimaryDomain, SubDomain, PathDomain) and Google’s parameters (PageRank, BackLink, GoogleIndex). Then, a proposed neurofuzzy model is a system which reduces the error and increases the performance. The neuro-fuzzy model uses computational models to perform without using if-then rule sets. The proposed technique achieved identification accuracy above 99% with low false signals.

The remainder of this paper is organized as follows: Section II presents the related works. System design is detailed 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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