Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity   Phishing websites are still a major threat in today's Internet ecosystem.
Despite numerous previous efforts, similarity-based detection methods do not
offer sufficient protection for the trusted websites - in particular against
unseen phishing pages. This paper contributes VisualPhishNet, a new
similarity-based phishing detection framework, based on a triplet Convolutional
Neural Network (CNN). VisualPhishNet learns profiles for websites in order to
detect phishing websites by a similarity metric that can generalize to pages
with new visual appearances. We furthermore present VisualPhish, the largest
dataset to date that facilitates visual phishing detection in an ecologically
valid manner. We show that our method outperforms previous visual similarity
phishing detection approaches by a large margin while being robust against a
range of evasion attacks.

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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
27
The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an