勒索软件检测的算法分割与行为画像：基于时间相关图
Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs
AI总结：
本文提出一种基于时间相关图的勒索软件检测框架，通过动态行为异常捕获与算法分割，在多种勒索软件上实现高精度检测，优于传统方法，并兼顾可扩展性与资源效率。
摘要：
网络威胁的快速演变已超越传统检测方法，亟需能够应对现代攻击者自适应且复杂行为的创新方案。本文提出了一种新框架，利用时间相关图对恶意操作中固有的复杂关系和时间模式进行建模。该方法动态捕获行为异常，为在实时场景中区分良性活动与恶意活动提供了稳健机制。大量实验表明，该框架在多种勒索软件家族上均有效，持续实现了高精确率、高召回率和整体检测准确率。对比评估显示，其性能优于传统的基于签名和启发式的方法，尤其在处理多态及未见过的勒索软件变种方面。该架构在设计时考虑了可扩展性和模块化，确保与企业级环境兼容，同时保持资源效率。对加密速度、异常模式和时间相关性的分析，为勒索软件的运营策略提供了更深入的见解，验证了该框架对不断演变威胁的适应性。这项研究通过整合动态图分析与机器学习，推动了网络安全技术的发展，为威胁检测的未来创新奠定了基础。研究结果凸显了该框架在转变组织检测和缓解复杂网络攻击方式上的潜力。
英文摘要：
The rapid evolution of cyber threats has outpaced traditional detection methodologies, necessitating innovative approaches capable of addressing the adaptive and complex behaviors of modern adversaries. A novel framework was introduced, leveraging Temporal-Correlation Graphs to model the intricate relationships and temporal patterns inherent in malicious operations. The approach dynamically captured behavioral anomalies, offering a robust mechanism for distinguishing between benign and malicious activities in real-time scenarios. Extensive experiments demonstrated the framework's effectiveness across diverse ransomware families, with consistently high precision, recall, and overall detection accuracy. Comparative evaluations highlighted its better performance over traditional signature-based and heuristic methods, particularly in handling polymorphic and previously unseen ransomware variants. The architecture was designed with scalability and modularity in mind, ensuring compatibility with enterprise-scale environments while maintaining resource efficiency. Analysis of encryption speeds, anomaly patterns, and temporal correlations provided deeper insights into the operational strategies of ransomware, validating the framework's adaptability to evolving threats. The research contributes to advancing cybersecurity technologies by integrating dynamic graph analytics and machine learning for future innovations in threat detection. Results from this study underline the potential for transforming the way organizations detect and mitigate complex cyberattacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
