Hybrid features-based prediction for novel phish websites
Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the exis...
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2016
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Online Access: | http://eprints.utm.my/id/eprint/74338/1/MazleenaSalleh2016_HybridFeaturesBasedPrediction.pdf http://eprints.utm.my/id/eprint/74338/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006427558&doi=10.11113%2fjt.v78.10026&partnerID=40&md5=e7f8f4998b87c2c6a53d3ead32965da0 |
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my.utm.743382017-11-22T12:07:41Z http://eprints.utm.my/id/eprint/74338/ Hybrid features-based prediction for novel phish websites Zuhair, H. Salleh, M. Selama, A. QA75 Electronic computers. Computer science Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the existing anti-phishing techniques. In this light, 58 new hybrid features were proposed in this paper and their prediction susceptibilities were evaluated by using feature co-occurrence criterion and a baseline machine learning algorithm. Empirical test and analysis showed the significant outcomes of the proposed features on detection performance. As a result, the most influential features are identified, and new insights are offered for further detection improvement. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/74338/1/MazleenaSalleh2016_HybridFeaturesBasedPrediction.pdf Zuhair, H. and Salleh, M. and Selama, A. (2016) Hybrid features-based prediction for novel phish websites. Jurnal Teknologi, 78 (12-3). pp. 95-109. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006427558&doi=10.11113%2fjt.v78.10026&partnerID=40&md5=e7f8f4998b87c2c6a53d3ead32965da0 |
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QA75 Electronic computers. Computer science Zuhair, H. Salleh, M. Selama, A. Hybrid features-based prediction for novel phish websites |
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Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the existing anti-phishing techniques. In this light, 58 new hybrid features were proposed in this paper and their prediction susceptibilities were evaluated by using feature co-occurrence criterion and a baseline machine learning algorithm. Empirical test and analysis showed the significant outcomes of the proposed features on detection performance. As a result, the most influential features are identified, and new insights are offered for further detection improvement. |
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Article |
author |
Zuhair, H. Salleh, M. Selama, A. |
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Zuhair, H. Salleh, M. Selama, A. |
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Zuhair, H. |
title |
Hybrid features-based prediction for novel phish websites |
title_short |
Hybrid features-based prediction for novel phish websites |
title_full |
Hybrid features-based prediction for novel phish websites |
title_fullStr |
Hybrid features-based prediction for novel phish websites |
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Hybrid features-based prediction for novel phish websites |
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hybrid features-based prediction for novel phish websites |
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Penerbit UTM Press |
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2016 |
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http://eprints.utm.my/id/eprint/74338/1/MazleenaSalleh2016_HybridFeaturesBasedPrediction.pdf http://eprints.utm.my/id/eprint/74338/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006427558&doi=10.11113%2fjt.v78.10026&partnerID=40&md5=e7f8f4998b87c2c6a53d3ead32965da0 |
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