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

Detecting Malicious URLs of COVID-19 Pandemic using ML technologies