Performing Ransomware Detection through Predictive Behavioral Mapping to Autonomous Threat Identification
Adam Blowing, Victor Stanislaw, Richard Wagner, Leonardo Ferrari, Stephen Magomedov · 2024
Escalating ransomware attacks present profound challenges to cybersecurity, particularly as attackers leverage increasingly sophisticated evasion techniques that render traditional detection methods inadequate. Predictive Behavioral Mapping (PBM) introduces a novel and adaptive approach to ransomware detection through sequential behavioral analysis, allowing for the identification of ransomware activities prior to payload execution. Utilizing recursive feature integration and a dynamic scoring algorithm, PBM enhances detection accuracy by predicting the progression of ransomware actions based on observed behavioral sequences, providing resilience against unknown ransomware variants that evade static signature-based methods. Experimental evaluations reveal that PBM achieves superior accuracy with reduced false positive rates, facilitating a proactive security model that operates efficiently within real-time infrastructures. Furthermore, PBM’s scalable design ensures low computational overhead across diverse data volumes, maintaining high performance in resource-constrained environments. Through its predictive and adaptable framework, PBM advances ransomware detection, enabling cybersecurity infrastructures to implement an efficient and anticipatory response to evolving threats.