A Novel Approach of Ransomware Detection with Dynamic Obfuscation Signature Analysis

The exponential rise in ransomware attacks has intensified the demand for sophisticated detection methodologies capable of addressing complex evasion tactics. Dynamic Obfuscation Signature Analysis (DOSA) offers an adaptive, multi-layered framework designed to counter ransomware’s polymorphic transformations through a hybrid approach that combines static analysis, dynamic signature mapping, and machine learning-based adaptation. DOSA’s architecture enhances detection accuracy and operational efficiency, with a modular design that supports real-time processing across file-based, network-based, and memory-based detection layers. Experiments demonstrated that DOSA achieved high accuracy rates, maintaining detection efficacy across a diverse array of ransomware variants by continuously evolving signature profiles based on obfuscation patterns. The framework also exhibited substantial resource efficiency, making it suitable for deployment in diverse environments with varied computational constraints. By providing precise and adaptable threat detection, DOSA contributes a significant advancement to the field of ransomware resilience, offering a robust methodology for preemptive ransomware management within modern cybersecurity infrastructures.