Ransomware Detection Through Contextual Behavior Mapping and Sequential Dependency Analysis

The escalating sophistication of cyber threats necessitates innovative detection methodologies to safeguard digital infrastructures. This research introduces a novel framework that integrates contextual behavior mapping with sequential dependency analysis, aiming to enhance the identification of both known and emerging ransomware variants. By employing probabilistic modeling and graph-based techniques, the proposed system effectively captures complex operational patterns inherent in ransomware activities. Extensive experiments conducted within controlled environments demonstrate the framework's high detection accuracy and low false positive rates, even when confronted with advanced evasion strategies. The scalability assessment reveals its capability to manage concurrent ransomware instances without significant performance degradation, showing its applicability in large-scale network infrastructures. Furthermore, the resource efficiency analysis indicates minimal computational overhead, facilitating seamless integration into existing security architectures. The robustness evaluation against polymorphic and metamorphic ransomware families highlights the framework's resilience, emphasizing the importance of adaptive detection mechanisms in contemporary cybersecurity landscapes. Collectively, these findings validate the proposed approach as a practical and efficient solution for ransomware detection, addressing critical challenges in modern security environments through innovative analytical methodologies and scalable design.