Predictive Profiling Framework for Ransomware Detection Using Contextual Signature Extraction

The evolving complexity of cybersecurity threats has driven the need for more advanced detection systems capable of addressing increasingly sophisticated ransomware attacks. This study introduces a novel framework built on contextual signature extraction, which analyzes dynamic system interactions to identify ransomware-specific behaviors before the encryption process begins. The framework’s modular architecture enables real-time processing, scalable deployment, and low-latency anomaly detection, making it highly adaptable to diverse operational environments. Unlike traditional signature-based and behavioral approaches, which often fail against polymorphic or obfuscated ransomware variants, the proposed system integrates advanced anomaly profiling and lightweight neural networks to achieve a balance between detection accuracy and computational efficiency. The evaluation demonstrates high accuracy and minimal false-positive rates across various ransomware families, including LockBit, BlackCat, and Hive, even under demanding workloads. Its ability to detect malicious activities at an early stage significantly reduces the risk of encryption-related data loss, offering a substantial improvement over existing techniques. Adversarial testing highlights the system’s robustness against evasion strategies, though further enhancements are needed to address extreme obfuscation and fileless attacks. The modular design ensures scalability and efficient resource utilization, making the framework suitable for both enterprise-level systems and resource-constrained environments. Insights into anomalous network behaviors and memory-resident attacks further demonstrate the versatility of the proposed approach. By combining real-time contextual analysis with adaptive anomaly detection, the framework establishes a new benchmark for ransomware detection and mitigation. The results highlight its potential to deliver robust and practical cybersecurity solutions, addressing both current and emerging challenges with precision and efficiency.