The increasing sophistication of cyber threats has necessitated the development of advanced detection mechanisms capable of identifying malicious activities with high precision and efficiency. A novel approach, termed Autonomous Feature Resonance, is introduced to address the limitations of traditional ransomware detection methods through the analysis of entropy-based feature interactions within system processes. The proposed method achieves an overall detection accuracy of 97.3\%, with false positive and false negative rates of 1.8\% and 2.1\%, respectively, outperforming existing techniques such as signature-based detection and behavioral analysis. Its decentralized architecture enables local processing of data, reducing latency and improving scalability, while a self-learning mechanism ensures continuous adaptation to emerging threats. Experimental results demonstrate consistent performance across diverse ransomware families, including LockBit 3.0, BlackCat, and Royal, with low detection latency and efficient resource utilization. The method's reliance on entropy as a distinguishing feature provides robustness against obfuscation techniques, making it suitable for real-time deployment in high-throughput environments. These findings highlight the potential of entropy-based approaches to enhance cybersecurity frameworks, offering a scalable and adaptive solution for modern ransomware detection challenges.

翻译：网络威胁日益复杂化，亟需开发能够以高精度和高效率识别恶意活动的高级检测机制。本文提出一种名为自主特征共振的新方法，通过分析系统进程中基于熵的特征交互，以解决传统勒索软件检测方法的局限性。所提方法实现了97.3%的整体检测准确率，误报率和漏报率分别为1.8%和2.1%，其性能优于基于特征的检测和行为分析等现有技术。其分布式架构支持数据的本地化处理，降低了延迟并提升了可扩展性，而自学习机制确保了对新兴威胁的持续适应能力。实验结果表明，该方法在包括LockBit 3.0、BlackCat和Royal在内的多种勒索软件家族上均表现出一致的性能，具有较低的检测延迟和高效的资源利用率。该方法以熵作为区分特征，使其对混淆技术具有鲁棒性，适合在高吞吐量环境中进行实时部署。这些发现凸显了基于熵的方法在增强网络安全框架方面的潜力，为应对现代勒索软件检测挑战提供了一种可扩展且自适应的解决方案。