Classification of Ransomware Attacks Using Machine Learning

Abstract

To make money, steal information, and harm computer systems, malware takes on a kind of dangerous presence in the online world. Ransomware is a unique form of virus that poses serious hazards to the entire planet. It has resulted in an immeasurable loss for the businesses, the government, and the people. The previous anti-malware technology employed signatures to detect malware when it came to creating a defense against it. However, once the ransomware has been installed on a victim's machine, further investigation is no longer feasible. The signature-based strategy has already started to lose its impact. Machine learning research and advancements in ransomware detection and classification have led to effective and precise differentiation. By gathering and studying ransomware characteristics, machine learning algorithms have significantly improved the ransomware defense technology. To discover the dataset with the greatest representation of ransomware behavior, this research will proceed from basic feature collections to feature engineering. Iterative techniques are being used to construct this system.