RANSOMWARE DETECTION AND CLASSIFICATION USING MACHINE LEARNING

Abstract

Ransomware has emerged as a widespread menace in the digital realm, inflicting considerable financial losses and disrupting vital services for both individuals and organizations. Traditional signature-based detection methods are proving inadequate against the ever-evolving strategies employed by cybercriminals. This research introduces an inventive strategy to counter ransomware threats by leveraging machine learning techniques for effective detection and classification. The study makes a valuable contribution to the ongoing cybersecurity efforts by presenting a resilient and adaptive solution for identifying and categorizing ransomware. Through the utilization of machine learning, this approach establishes a proactive defense mechanism against ransomware threats, ensuring the protection of sensitive data, financial resources, and critical infrastructure from malicious attacks in the contemporary digital landscape.