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

Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection