MACHINE LEARNING-BASED TECHNIQUES FOR EFFECTIVE RANSOMWARE DETECTION

Abstract

The need of effective and dynamic detection and mitigation techniques is growing in light of the increasing frequency and sophistication of ransomware assaults. Often, traditional mark-based methods are inadequate in identifying new and evolving ransomware variants. In order to increase the accuracy and adaptability of detection tools, this study explores the use of machine learning techniques for ransomware detection. It offers a thorough analysis of several machine learning techniques and algorithms, evaluating how well they detect ransomware trends. The findings provide crucial information for creating cybersecurity plans that are more robust and proactive in addressing the ever-changing ransomware threat scenario.