  Throughout the COVID-19 outbreak, malicious attacks have become more
pervasive and damaging than ever. Malicious intruders have been responsible for
most cybercrimes committed recently and are the cause for a growing number of
cyber threats, including identity and IP thefts, financial crimes, and
cyber-attacks to critical infrastructures. Machine learning (ML) has proven
itself as a prominent field of study over the past decade by solving many
highly complex and sophisticated real-world problems. This paper proposes an
ML-based classification technique to detect the growing number of malicious
URLs, due to the COVID-19 pandemic, which is currently considered a threat to
IT users. We have used a large volume of Open Source data and preprocessed it
using our developed tool to generate feature vectors and we trained the ML
model using the apprehensive malicious threat weight. Our ML model has been
tested, with and without entropy to forecast the threatening factors of
COVID-19 URLs. The empirical evidence proves our methods to be a promising
mechanism to mitigate COVID-19 related threats early in the attack lifecycle.
