Detecting malicious COVID-19 URLs using machine learning techniques

Ispahany, J.; Islam, R.; Ieee,

Throughout the COVID-19 outbreak, malicious attacks have become more pervasive and damaging than ever. Malicious intruders have been responsible for most of the 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 due to solving 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 trained the ML model using an 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.