A Comprehensive Analysis of Machine Learning-Based File Trap Selection Methods to Detect Crypto Ransomware

This research paper examines methods that use machine learning (algorithms that learn patterns from data) to identify and trap crypto ransomware (malicious software that encrypts files and demands payment) by analyzing how files behave. The study focuses on evaluating different techniques for selecting which files should be monitored as decoys to detect ransomware attacks before they cause damage.