An Innovative Framework for Ransomware Detection Using Entropic Trace Profiling and Temporal Signature Mapping

The evolution of encryption-based cyber threats has posed significant challenges to existing detection methodologies, necessitating innovative approaches that can adapt to dynamic attack patterns. A dual-component framework was introduced, combining Entropic Trace Profiling and Temporal Signature Mapping to provide a robust mechanism for identifying encryption and behavioral anomalies indicative of malicious activity. Entropy profiling quantified randomness in data structures, enabling early identification of encryption-based threats, while temporal analysis captured irregularities in execution timing and system interactions. The integration of these methods yielded a cohesive detection system capable of addressing diverse ransomware families with high accuracy and efficiency. Experiments demonstrated that the framework maintained detection rates above 94\%, with false positive rates consistently below 4\%, validating its robustness across varying operational scenarios. The modular architecture of the framework facilitated scalability and adaptability, making it suitable for both enterprise and individual deployment contexts. Comparative analysis highlighted its advantages over traditional signature-based and heuristic approaches, particularly in detecting polymorphic and obfuscated threats. Computational performance assessments revealed low resource consumption, ensuring compatibility with constrained environments such as IoT and embedded systems. The framework achieved resilience against evasion techniques, including encryption noise injection and delayed execution tactics, underscoring its ability to counter adaptive threats. Entropy and temporal insights were combined through a weighted decision engine, balancing precision and sensitivity to minimize operational disruptions. The study contributes to advancing detection methodologies by bridging the gap between static and dynamic analysis approaches. The findings highlight the potential of algorithmic innovations in enhancing cybersecurity defenses against encryption-based attacks.