Novel Algorithmic Framework for Ransomware Detection via Contextual Flow Anomaly Mapping

Emerging cyber threats require advanced frameworks capable of identifying and mitigating highly sophisticated attack vectors. The proposed detection methodology leverages Contextual Flow Anomaly Mapping to dynamically analyze behavioral patterns, allowing precise identification of malicious activity, including ransomware variants such as LockBit and REvil. By integrating machine learning algorithms with advanced feature extraction and dimensionality reduction techniques, the framework achieves a balance between high detection accuracy and computational efficiency. Scalability and modularity enhance its applicability across diverse environments, ranging from resource-constrained systems to enterprise networks, while maintaining consistent performance metrics. Experimental evaluations demonstrate robust detection capabilities, low latency under heavy network loads, and effective memory utilization. Comprehensive analysis of behavioral features highlights the adaptability of the approach in detecting obfuscated and fileless ransomware activities. The framework’s comparison with traditional detection models reveals significant advancements in sensitivity and specificity, offering a viable solution to real-time security challenges. Insights into energy efficiency and the handling of high-speed encryption scenarios demonstrate its practicality for modern digital ecosystems. This research introduces a powerful mechanism to reinforce cybersecurity resilience through proactive and adaptive measures, significantly mitigating the risks posed through evolving ransomware tactics.