System-Wide Ransomware Attack Detection via Perturbation-Resilient Representation Learning

Ransomware (malicious software that encrypts files to extort money) has become more sophisticated by spreading attacks across multiple programs, making traditional process-level detection ineffective. This research presents CoLPR, a machine learning framework that learns to detect ransomware by understanding which behaviors are truly malicious versus which are just normal variations from different applications running together, achieving 100% detection success rate with only a 5-second delay.