Real-Time Ransomware Detection Through Adaptive Behavior Fingerprinting for Improved Cybersecurity Resilience and Defense
Richard Hurley, Philip Kruger, Henry Nascimento, Stephen C. Keller · 2024
The growing frequency and sophistication of ransomware attacks have posed substantial challenges for traditional detection mechanisms, which often struggle to keep pace with the adaptive strategies employed by modern ransomware variants. Introducing a novel approach, Adaptive Behavior Fingerprinting (ABF), enables a more dynamic and resilient ransomware detection model through the continuous tracking and classification of behavioral patterns unique to ransomware. ABF’s architecture leverages behavior-driven analytics integrated directly at the system kernel level, allowing for uninterrupted monitoring of system activities such as file access operations, registry modifications, and network behaviors. Employing advanced clustering algorithms and adaptive decision trees, the ABF system adjusts its detection thresholds in response to real-time variations in ransomware behaviors, thus maintaining high detection accuracy even as ransomware evolves to evade traditional methods. Testing results demonstrate ABF’s effectiveness across multiple ransomware families, achieving consistent detection accuracy with low false positive rates and minimal processing overhead. The findings underscore ABF’s significant potential for practical applications, offering enhanced reliability and adaptability in ransomware defense, especially within high-risk environments where rapid and precise threat detection is critical.