Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion
Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly, that becomes a serious web threat and puts both users and enterprises at the risks of identity theft and monetary losses day by day. In the literature, most computational efforts were dedicated to just...
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| Format: | Article |
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Asian Research Publishing Network (ARPN)
2015
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| _version_ | 1796860145476567040 |
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| author | Zuhair, Hiba Selamat, Ali Thanh Salleh, Mazleena |
| author_facet | Zuhair, Hiba Selamat, Ali Thanh Salleh, Mazleena |
| author_sort | Zuhair, Hiba |
| collection | ePrints |
| description | Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly, that becomes a serious web threat and puts both users and enterprises at the risks of identity theft and monetary losses day by day. In the literature, most computational efforts were dedicated to justify well-performed phishing detection against evolving phish exploits. However, facets like exploration of new and predictive features, selecting minimal and robust features compactness still raise as key challenges to optimize the detection scenarios over vast and strongly interrelated web. In this study, we proposed a set of new hybrid features, and refine it as few, maximum relevant, minimum redundant, and robust features as possible. In the presence of a machine learning classifier and some assessment criteria that recommended for this purpose, the reported results experimentally demonstrated that our remedial scenario could be used to optimize a phish detection model for any anti-phishing scheme in the future. |
| first_indexed | 2024-03-05T19:37:35Z |
| format | Article |
| id | utm.eprints-55407 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:37:35Z |
| publishDate | 2015 |
| publisher | Asian Research Publishing Network (ARPN) |
| record_format | dspace |
| spelling | utm.eprints-554072016-09-04T02:27:21Z http://eprints.utm.my/55407/ Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion Zuhair, Hiba Selamat, Ali Thanh Salleh, Mazleena QA75 Electronic computers. Computer science Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly, that becomes a serious web threat and puts both users and enterprises at the risks of identity theft and monetary losses day by day. In the literature, most computational efforts were dedicated to justify well-performed phishing detection against evolving phish exploits. However, facets like exploration of new and predictive features, selecting minimal and robust features compactness still raise as key challenges to optimize the detection scenarios over vast and strongly interrelated web. In this study, we proposed a set of new hybrid features, and refine it as few, maximum relevant, minimum redundant, and robust features as possible. In the presence of a machine learning classifier and some assessment criteria that recommended for this purpose, the reported results experimentally demonstrated that our remedial scenario could be used to optimize a phish detection model for any anti-phishing scheme in the future. Asian Research Publishing Network (ARPN) 2015-11 Article PeerReviewed Zuhair, Hiba and Selamat, Ali Thanh and Salleh, Mazleena (2015) Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion. Journal of Theoretical and Applied Information Technology, 81 (2). pp. 188-205. ISSN 1992-8645 |
| spellingShingle | QA75 Electronic computers. Computer science Zuhair, Hiba Selamat, Ali Thanh Salleh, Mazleena Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title | Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title_full | Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title_fullStr | Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title_full_unstemmed | Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title_short | Selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| title_sort | selection of robust feature subsets for phish webpage prediction using maximum relevance and minimum redundancy criterion |
| topic | QA75 Electronic computers. Computer science |
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