面向多源异构数据的网络安全威胁智能分析与动态响应：研究综述与展望

Intelligent Analysis and Dynamic Response of Cybersecurity Threats for Multi-Source Heterogeneous Data: A Review and Prospect

摘要: 随着网络攻击手段复合化与防御孤岛化，如何基于多源异构数据实现网络安全威胁的智能分析与动态响应，已成为态势感知与主动防御的核心议题。多源安全数据在来源、结构与质量上高度异构，其冗余、误报与标注稀缺构成框架性能的上限约束。围绕“感知–识别–响应”闭环，D-S证据理论与模糊/贝叶斯推理仍是融合不确定、不完备证据的主流范式，机器学习与知识图谱分别从数据驱动与语义推理维度予以补充；事件定级、自动化缓解、访问控制与平台化协同共同构成动态响应链条。当前框架的可信落地仍受数据质量治理、模型可解释性、自动化误伤与对抗鲁棒性四重挑战制约，未来应从追求更高自动率转向追求可验证的可信度。

Abstract: As cyber attacks grow increasingly composite and defense devices remain fragmented into isolated silos, how to achieve intelligent analysis and dynamic response of cybersecurity threats based on multi-source heterogeneous data has become a core issue in situational awareness and active defense. Multi-source security data is highly heterogeneous in origin, structure, and quality; its redundancy, false positives, and label scarcity set a hard upper bound on any fusion or detection model. Around the sense-identify-respond loop, D-S evidence theory and fuzzy/Bayesian reasoning remain the dominant paradigms for fusing incomplete and uncertain evidence, while machine learning and knowledge graphs progressively contribute data-driven detection and semantic attribution. Event grading, automated mitigation, access control, and platform-centric collaboration form the dynamic response chain. Yet the current framework’s trustworthy deployment is constrained by four challenges: data-quality governance, model interpretability, false-positive misinjury in automated response, and adversarial robustness. In the future, efforts should shift from pursuing higher automation rates to achieving verifiable credibility.