中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Two-stage ELM for phishing Web pages detection using hybrid features

文献类型:期刊论文

作者Wei Zhang; Qingshan Jiang; Lifei Chen; Chengming Li
刊名WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版日期2017
文献子类期刊论文
英文摘要Increasing high volume phishing attacks are being encountered every day due to attackers' high financial returns. Recently, there has been significant interest in applying machine learning for phishing Web pages detection. Different from literatures, this paper introduces predicted labels of textual contents to be part of the features and proposes a novel framework for phishing Web pages detection using hybrid features consisting of URL-based, Web-based, rule-based and textual content-based features. We achieve this framework by developing an efficient two-stage extreme learning machine (ELM). The first stage is to construct classification models on textual contents of Web pages using ELM. In particular, we take Optical Character Recognition (OCR) as an assistant tool to extract textual contents from image format Web pages in this stage. In the second stage, a classification model on hybrid features is developed by using a linear combination model-based ensemble ELMs (LC-ELMs), with the weights calculated by the generalized inverse. Experimental results indicate the proposed framework is promising for detecting phishing Web pages.
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语种英语
源URL[http://ir.siat.ac.cn:8080/handle/172644/12562]  
专题深圳先进技术研究院_数字所
作者单位WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
推荐引用方式
GB/T 7714
Wei Zhang,Qingshan Jiang,Lifei Chen,et al. Two-stage ELM for phishing Web pages detection using hybrid features[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2017.
APA Wei Zhang,Qingshan Jiang,Lifei Chen,&Chengming Li.(2017).Two-stage ELM for phishing Web pages detection using hybrid features.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS.
MLA Wei Zhang,et al."Two-stage ELM for phishing Web pages detection using hybrid features".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2017).

入库方式: OAI收割

来源:深圳先进技术研究院

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