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      Web Phishing Detection Using a Deep Learning Framework

      1 , 1 , 1 , 2 , 2 , 2

      Wireless Communications and Mobile Computing

      Hindawi Limited

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          Abstract

          Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

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          Most cited references 17

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           R. Neal,  B. Frey,  P. Dayan (1995)
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            Security Framework for Wireless Communications in Smart Distribution Grid

             Ping Yi,  Xudong Wang (2011)
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              Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)

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                Author and article information

                Journal
                Wireless Communications and Mobile Computing
                Wireless Communications and Mobile Computing
                Hindawi Limited
                1530-8669
                1530-8677
                September 26 2018
                September 26 2018
                : 2018
                : 1-9
                Affiliations
                [1 ]School of Cyber Security, Shanghai Jiao Tong University, Shanghai 200240, China
                [2 ]Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD 21250, USA
                Article
                10.1155/2018/4678746
                © 2018

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