دانلود رایگان ترجمه مقاله یک رویکرد عصبی فازی برای شناسایی فیشینگ (نشریه IEEE 2014)

یک رویکرد عصبی فازی برای شناسایی فیشینگ

A Novel Neuro-Fuzzy Approach for Phishing Identification

Abstract

Together with the growth of Internet, e-commerce transactions play an important role in the modern society. As a result, phishing is a deliberate act by an individual or a group of people to steal personal information such as password, banking account, 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 identify phishing websites, such as Blacklist-based technique, Heuristic-based technique, etc. However, the number of victims has 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 identification. Specifically, the proposed technique calculates the value of heuristics from membership functions. Then, the weights are trained by 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 identify over 99% phishing sites.

1 Introduction

The word ”phishing” is produced from the word ”fishing”. Phishers, creating phishing sites, use a number of techniques to fool their victims, including email messages, instant messages, forum posts, phone calls and social networking. With these activities of phishing, it causes severe economy loss all over the world. According to a study by Gartner [1], 57 million US Internet users have identified the receipt of email linked to phishing scams and about 2 million of them are estimated to have been tricked into giving away sensitive information. 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, identifying phishing problem is very urgent, complex and extremely important problem in modern society. Recently, there have been many studies that 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 interface of website, etc. However, each of studies has its own strengths and weaknesses. There is still not a sufficient method. In this paper, a new approach is proposed to identify 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 identification 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.