A Novel Methodology for Automated Ransomware Detection via Deep Behavioral Sequence Mapping
Courtney Boyd, Stephen Johansson, Charles McAllister · 2024
Escalating ransomware attacks have emerged as a critical challenge for cybersecurity, with conventional detection techniques struggling to maintain effectiveness against increasingly sophisticated and evasive ransomware strains. Introducing a paradigm shift in detection methodology, Deep Behavioral Sequence Mapping (DBSM) leverages machine learning to analyze complex behavioral patterns rather than static indicators, offering an adaptive and robust framework for identifying ransomware. Unlike traditional approaches that rely on predefined signatures or rule sets, DBSM processes dynamic, sequential behaviors to detect ransomware even when obfuscation techniques are applied. Comprehensive evaluations demonstrate that DBSM achieves superior accuracy, precision, and recall, significantly reducing false positives while optimizing detection efficiency. Its scalability and low-latency processing enable real-time applications across enterprise-level environments, highlighting DBSM's potential as a critical component in automated threat defense systems. The study’s findings suggest DBSM’s capacity to enhance detection resilience and establish a new standard for ransomware threat mitigation within high-risk, large-scale infrastructures.