  Domain Name System (DNS) plays in important role in the current IP-based
Internet architecture. This is because it performs the domain name to IP
resolution. However, the DNS protocol has several security vulnerabilities due
to the lack of data integrity and origin authentication within it. This paper
focuses on one particular security vulnerability, namely typo-squatting.
Typo-squatting refers to the registration of a domain name that is extremely
similar to that of an existing popular brand with the goal of redirecting users
to malicious/suspicious websites. The danger of typo-squatting is that it can
lead to information threat, corporate secret leakage, and can facilitate fraud.
This paper builds on our previous work in [1], which only proposed
majority-voting based classifier, by proposing an ensemble-based feature
selection and bagging classification model to detect DNS typo-squatting attack.
Experimental results show that the proposed framework achieves high accuracy
and precision in identifying the malicious/suspicious typo-squatting domains (a
loss of at most 1.5% in accuracy and 5% in precision when compared to the model
that used the complete feature set) while having a lower computational
complexity due to the smaller feature set (a reduction of more than 50% in
feature set size).
