Semantic Entanglement-Based Ransomware Detection via Probabilistic Latent Encryption Mapping

Abstract

Encryption-based attacks have introduced significant challenges for detection mechanisms that rely on predefined signatures, heuristic indicators, or static rule-based classifications. Probabilistic Latent Encryption Mapping presents an alternative detection framework that models ransomware-induced encryption behaviors through statistical representations of entropy deviations and probabilistic dependencies in execution traces. Unlike conventional approaches that depend on explicit bytecode analysis or predefined cryptographic function call monitoring, probabilistic inference techniques classify encryption anomalies based on their underlying statistical characteristics, ensuring greater adaptability to polymorphic attack strategies. Evaluations demonstrate that entropy-driven classification reduces false positive rates while maintaining high detection accuracy across diverse ransomware families and encryption methodologies. Experimental results further highlight the framework's ability to differentiate between benign encryption workflows and adversarial cryptographic manipulations, ensuring that classification performance remains effective across cloud-based and localized execution environments. Benchmark comparisons illustrate that probabilistic modeling exhibits advantages over heuristic and machine learning-based detection approaches, particularly in handling previously unseen encryption techniques and adversarial obfuscation strategies. Computational efficiency analysis confirms that detection latency remains within operational feasibility constraints, reinforcing the viability of probabilistic encryption classification for real-time security infrastructures. The ability to systematically infer encryption-induced deviations without requiring static attack signatures strengthens detection robustness against adversarial evasion techniques.