Integrated Dynamic Behavioral Fingerprinting for Real-Time Ransomware Detection: A Pattern-Based Classification
Jasper Beaumarchais, Patrick Weber, Nicholas Callaghan, Gregory Bauer · 2024
The increasing sophistication and prevalence of ransomware attacks pose a substantial risk to digital infrastructures, with the potential to disrupt critical operations and compromise sensitive information across various sectors. Traditional detection methods, often based on static analysis or signature-based techniques, struggle to adapt to the rapidly evolving nature of ransomware variants, necessitating a more dynamic and adaptable approach. This study presents a novel methodology known as Dynamic Behavioral Fingerprinting, developed to identify ransomware activities in real-time through the examination of distinctive behavioral patterns exhibited during application execution. The proposed approach addresses key limitations of existing detection methods through the analysis of runtime behaviors, enabling effective identification of unknown or polymorphic ransomware strains that would otherwise evade static detection. Experimental evaluations highlight the method's high accuracy, precision, and resilience against evasion tactics, showing its utility in maintaining robust cybersecurity defenses. Furthermore, results indicate that the system operates with minimal resource consumption, allowing for scalable deployment in real-time environments without significant impact on host performance. These findings suggest that Dynamic Behavioral Fingerprinting provides an effective and efficient solution to the challenges posed by modern ransomware threats, enhancing the capability of security frameworks to respond promptly and decisively to potential incidents.