  A visual homograph attack is a way that the attacker deceives the web users
about which domain they are visiting by exploiting forged domains that look
similar to the genuine domains. T. Thao et al. (IFIP SEC'19) proposed a
homograph classification by applying conventional supervised learning
algorithms on the features extracted from a single-character-based Structural
Similarity Index (SSIM). This paper aims to improve the classification accuracy
by combining their SSIM features with 199 features extracted from a N-gram
model and applying advanced ensemble learning algorithms. The experimental
result showed that our proposed method could enhance even 1.81% of accuracy and
reduce 2.15% of false-positive rate. Furthermore, existing work applied machine
learning on some features without being able to explain why applying it can
improve the accuracy. Even though the accuracy could be improved, understanding
the ground-truth is also crucial. Therefore, in this paper, we conducted an
error empirical analysis and could obtain several findings behind our proposed
approach.
