Intelligent Visual Similarity-Based Phishing Websites Detection

Jiann-Liang Chen; Yi-Wei Ma; Kuan-Lung Huang

doi:10.3390/sym12101681

Symmetry (Oct 2020)

Intelligent Visual Similarity-Based Phishing Websites Detection

Jiann-Liang Chen,
Yi-Wei Ma,
Kuan-Lung Huang

Affiliations

Jiann-Liang Chen: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Yi-Wei Ma: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Kuan-Lung Huang: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan

DOI: https://doi.org/10.3390/sym12101681
Journal volume & issue: Vol. 12, no. 10
p. 1681

Abstract

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This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of ‘very similar’, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of ‘local similarity’, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns ‘very similar’ and ‘local similar’ cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.

Published in Symmetry

ISSN: 2073-8994 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Mathematics
Website: http://www.mdpi.com/journal/symmetry/

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