  The escalating landscape of cyber threats, characterized by the registration
of thousands of new domains daily for large-scale Internet attacks such as
spam, phishing, and drive-by downloads, underscores the imperative for
innovative detection methodologies. This paper introduces a cutting-edge
approach for identifying suspicious domains at the onset of the registration
process. The accompanying data pipeline generates crucial features by comparing
new domains to registered domains, emphasizing the crucial similarity score.
The proposed system analyzes semantic and numerical attributes by leveraging a
novel combination of Natural Language Processing (NLP) techniques, including a
pretrained CANINE model and Multilayer Perceptron (MLP) models, providing a
robust solution for early threat detection. This integrated Pretrained NLP
(CANINE) + MLP model showcases the outstanding performance, surpassing both
individual pretrained NLP models and standalone MLP models. With an F1 score of
84.86\% and an accuracy of 84.95\% on the SecureReg dataset, it effectively
detects malicious domain registrations. The findings demonstrate the
effectiveness of the integrated approach and contribute to the ongoing efforts
to develop proactive strategies to mitigate the risks associated with illicit
online activities through the early identification of suspicious domain
registrations.
