A Computational Model for Precision Ransomware Detection Using Vectorised Behavior Graphs

As cyberattacks grow increasingly complex, addressing advanced threats requires approaches that adapt to dynamic and unpredictable behaviors. A new framework is introduced that models malicious activities through Vectorised Behavior Graphs, capturing the intricate relationships and temporal patterns of ransomware operations. By transforming these behaviors into structured graph representations, the system enables precise detection through machine learning techniques, achieving high accuracy while maintaining computational efficiency. The integration of attention mechanisms enhances the interpretability of the classification process, shedding light on critical indicators such as encryption patterns, file system interactions, and process anomalies. Graph-based embeddings serve as a foundation for understanding ransomware characteristics, making it possible to detect novel variants that evade traditional methods. Experimental evaluations highlight the system’s robustness across a variety of ransomware families and its scalability in handling large datasets. Results also underscore its adaptability in environments where low latency is essential, such as real-time applications. An analysis of feature dimensionality reveals how optimized data representations contribute to improved performance without compromising efficiency. False positive assessments provide insights into potential areas for refinement, particularly in distinguishing between ransomware and benign applications with overlapping behaviors. The modularity of the framework supports easy updates, ensuring its relevance as attack tactics evolve. With strong performance across diverse metrics, the findings offer an effective strategy for enhancing ransomware detection capabilities and advancing cybersecurity resilience.