Abstract
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.
Highlights
With the rapid development of information technology, many Internet applications are attracting increasing attention and becoming part of everyday life
The results of the experiments reveal that the wavelet Hashing (wHash) mechanism with a color histogram is more accurate than the currently used perceptual Hashing mechanism
This study develops the Scale-Invariant Feature Transform (SIFT) image object detection technique to detect whether a logo has been extracted from the screenshot of a whole webpage
Summary
With the rapid development of information technology, many Internet applications are attracting increasing attention and becoming part of everyday life. It brings all kinds of online scams. Phishing is a scam that uses social engineering to induce users to disclose personal and private information [1]. Report, the average number of phishing websites that can be detected per month is as high as 55,232 [2]. This work proposes an approach to detecting phishing websites that is based on visual similarity.
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