This research introduces a data-driven approach to help organizations identify which ransomware threats are most likely to target them, enabling more focused defense strategies.

• Analyzes patterns from historical ransomware victim data to predict future targets
• Develops machine learning models to match potential victims with likely ransomware groups
• Provides a framework for risk-based prioritization of security resources
• Enables organizations to focus defenses against their most probable threats

This approach matters because it transforms generic ransomware defense into targeted preparation against specific threat actors, dramatically improving security resource allocation in the face of escalating ransomware incidents.

Assessing and Prioritizing Ransomware Risk Based on Historical Victim Data