  Cyber attacks deceive machines into believing something that does not exist
in the first place. However, there are some to which even humans fall prey. One
such famous attack that attackers have used over the years to exploit the
vulnerability of vision is known to be a Homoglyph attack. It employs a primary
yet effective mechanism to create illegitimate domains that are hard to
differentiate from legit ones. Moreover, as the difference is pretty
indistinguishable for a user to notice, they cannot stop themselves from
clicking on these homoglyph domain names. In many cases, that results in either
information theft or malware attack on their systems. Existing approaches use
simple, string-based comparison techniques applied in primary language-based
tasks. Although they are impactful to some extent, they usually fail because
they are not robust to different types of homoglyphs and are computationally
not feasible because of their time requirement proportional to the string
length. Similarly, neural network-based approaches are employed to determine
real domain strings from fake ones. Nevertheless, the problem with both methods
is that they require paired sequences of real and fake domain strings to work
with, which is often not the case in the real world, as the attacker only sends
the illegitimate or homoglyph domain to the vulnerable user. Therefore,
existing approaches are not suitable for practical scenarios in the real world.
In our work, we created GlyphNet, an image dataset that contains 4M domains,
both real and homoglyphs. Additionally, we introduce a baseline method for a
homoglyph attack detection system using an attention-based convolutional Neural
Network. We show that our model can reach state-of-the-art accuracy in
detecting homoglyph attacks with a 0.93 AUC on our dataset.
