Web Phishing Detection Using a Deep Learning Framework
Joint Authors
Yi, Ping
Zou, Futai
Zhu, Ting
Yao, Yao
Wang, Wei
Guan, Yuxiang
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yi, Ping& Guan, Yuxiang& Zou, Futai& Yao, Yao& Wang, Wei& Zhu, Ting. 2018. Web Phishing Detection Using a Deep Learning Framework. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1216041
Modern Language Association (MLA)
Yi, Ping…[et al.]. Web Phishing Detection Using a Deep Learning Framework. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1216041
American Medical Association (AMA)
Yi, Ping& Guan, Yuxiang& Zou, Futai& Yao, Yao& Wang, Wei& Zhu, Ting. Web Phishing Detection Using a Deep Learning Framework. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1216041
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1216041