Malicious encryption techniques continue to evolve, bypassing conventional detection mechanisms that rely on static signatures or predefined behavioral rules. Spectral analysis presents an alternative approach that transforms system activity data into the frequency domain, enabling the identification of anomalous waveform signatures that are difficult to obfuscate through traditional evasion techniques. The proposed Spectral Entanglement Fingerprinting (SEF) framework leverages power spectral densities, coherence functions, and entropy-based metrics to extract hidden patterns indicative of unauthorized encryption activities. Detection accuracy evaluations demonstrate that frequency-domain transformations achieve superior performance in distinguishing malicious from benign processes, particularly in the presence of polymorphic and metamorphic modifications. Comparative analyses with established methods reveal that frequency-based detection minimizes false positive and false negative rates, ensuring operational efficiency without excessive computational overhead. Experimental results indicate that entropy variations in encrypted data streams provide meaningful classification insights, allowing the differentiation of distinct ransomware families based on spectral characteristics alone. The latency assessment confirms that SEF operates within a time window that enables proactive intervention, mitigating encryption-induced damage before data integrity is compromised. Scalability evaluations suggest that the framework remains effective even under concurrent execution of multiple ransomware instances, supporting its suitability for high-throughput environments.

翻译：恶意加密技术持续演进，能够规避依赖静态特征或预定义行为规则的传统检测机制。谱分析提供了一种替代方法，将系统活动数据转换至频域，从而能够识别难以通过传统规避技术混淆的异常波形特征。所提出的谱纠缠指纹识别（SEF）框架利用功率谱密度、相干函数和基于熵的度量来提取指示未经授权加密活动的隐藏模式。检测准确性评估表明，频域变换在区分恶意进程与良性进程方面表现出优越性能，尤其是在存在多态和变形修改的情况下。与现有方法的比较分析表明，基于频率的检测能够最小化误报率和漏报率，在确保运行效率的同时不会产生过高的计算开销。实验结果表明，加密数据流中的熵变化提供了有意义的分类依据，仅凭谱特征即可区分不同的勒索软件家族。延迟评估证实，SEF在允许主动干预的时间窗口内运行，能够在数据完整性受损前减轻加密造成的损害。可扩展性评估表明，即使在多个勒索软件实例并发执行的情况下，该框架仍保持有效性，证明其适用于高吞吐量环境。