  Domain Name System (DNS) is a crucial component of current IP-based networks
as it is the standard mechanism for name to IP resolution. However, due to its
lack of data integrity and origin authentication processes, it is vulnerable to
a variety of attacks. One such attack is Typosquatting. Detecting this attack
is particularly important as it can be a threat to corporate secrets and can be
used to steal information or commit fraud. In this paper, a machine
learning-based approach is proposed to tackle the typosquatting vulnerability.
To that end, exploratory data analytics is first used to better understand the
trends observed in eight domain name-based extracted features. Furthermore, a
majority voting-based ensemble learning classifier built using five
classification algorithms is proposed that can detect suspicious domains with
high accuracy. Moreover, the observed trends are validated by studying the same
features in an unlabeled dataset using K-means clustering algorithm and through
applying the developed ensemble learning classifier. Results show that
legitimate domains have a smaller domain name length and fewer unique
characters. Moreover, the developed ensemble learning classifier performs
better in terms of accuracy, precision, and F-score. Furthermore, it is shown
that similar trends are observed when clustering is used. However, the number
of domains identified as potentially suspicious is high. Hence, the ensemble
learning classifier is applied with results showing that the number of domains
identified as potentially suspicious is reduced by almost a factor of five
while still maintaining the same trends in terms of features' statistics.
