Detecting encryption-driven cyber threats remains a large challenge due to the evolving techniques employed to evade traditional detection mechanisms. An entropy-based computational framework was introduced to analyze multi-domain system variations, enabling the identification of malicious encryption behaviors through entropy deviations. By integrating entropy patterns across file operations, memory allocations, and network transmissions, a detection methodology was developed to differentiate between benign and ransomware-induced entropy shifts. A mathematical model was formulated to quantify entropy dynamics, incorporating time-dependent variations and weighted domain contributions to enhance anomaly detection. Experimental evaluations demonstrated that the proposed approach achieved high accuracy across diverse ransomware families while maintaining low false positive rates. Computational efficiency analysis indicated minimal processing overhead, suggesting feasibility for real-time implementation in security-sensitive environments. The study highlighted entropy fluctuations as a useful indicator for identifying malicious encryption processes, reinforcing entropy-driven methodologies as a viable component of cybersecurity strategies.

翻译：检测加密驱动的网络威胁仍然是一个重大挑战，这归因于不断演变的规避传统检测机制的技术。本文引入了一种基于熵的计算框架，用于分析多领域系统变化，从而通过熵偏差识别恶意加密行为。通过整合文件操作、内存分配和网络传输中的熵模式，开发了一种检测方法，以区分良性和勒索软件引起的熵变化。建立了一个数学模型来量化熵动态，该模型结合了时间依赖性变化和加权领域贡献，以增强异常检测。实验评估表明，所提方法在多种勒索软件家族中实现了高准确率，同时保持了较低的误报率。计算效率分析表明处理开销极小，暗示了在安全敏感环境中实时实施的可行性。该研究强调了熵波动作为识别恶意加密过程的有效指标，进一步证实了熵驱动方法作为网络安全策略可行组成部分的价值。