A domain-feature enhanced classification model for the detection of Chinese phishing e-Business websites |
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| Authors: | Dongsong Zhang Zhijun Yan Hansi Jiang Taeha Kim |
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| Affiliation: | 1. Department of Information Systems, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA;2. School of Management & Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China;3. Department of Management Information Systems, College of Business & Economics, Chung-Ang University, 84 HeukSeok-Ro, Dongjak-Gu, Seoul, Korea 156-756 |
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| Abstract: | We propose a novel classification model that consists of features of website URLs and content for automatically detecting Chinese phishing e-Business websites. The model incorporates several unique domain-specific features of Chinese e-Business websites. We evaluated the proposed model using four different classification algorithms and approximately 3,000 Chinese e-Business websites. The results show that the Sequential Minimal Optimization (SMO) algorithm performs the best. The proposed model outperforms two baseline models in detection precision, recall, and F-measure. The results of a sensitivity analysis demonstrate that domain-specific features have the most significant impact on the detection of Chinese phishing e-Business websites. |
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| Keywords: | Phishing websites E-business Classification Detection Feature vectors |
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