威胁情报知识图谱的研究现状与未来趋势  

Research Status and Future Trends of Threat Intelligence Knowledge Graph

作　　者：涂晨 

机构地区：[1]江西理工大学信息工程学院,江西 赣州

出　　处：《计算机科学与应用》2025年第2期153-158,共6页Computer Science and Application

摘　　要：随着物联网的迅速发展,安全问题日益严峻,恶意攻击者通过物联网设备进行攻击,导致数据泄漏。知识图谱通过深度挖掘和关联分析,在安全态势感知和威胁预测中发挥了重要作用。构建物联网威胁情报知识图谱可以整合设备信息、网络流量、攻击事件等多源数据,识别威胁模式,预测潜在攻击路径,实现实时威胁检测与响应。知识图谱的推理能力还能够发现新的威胁关系和异常行为,提升智能防护水平。本文综述从网络安全信息抽取、知识本体建模和图谱存储和推理四个方面展开,探讨了现状和未来趋势,旨在推动知识图谱在物联网安全中的应用。With the rapid development of the Internet of Things, security problems are becoming increasingly severe, and malicious attackers attack through IoT devices, resulting in data leakage. Knowledge graphs play an important role in security situational awareness and threat prediction through deep mining and correlation analysis. Building an IoT threat intelligence knowledge graph can integrate multi-source data such as device information, network traffic, and attack events, identify threat patterns, predict potential attack paths, and achieve real-time threat detection and response. The reasoning capability of the knowledge graph can also discover new threat relationships and abnormal behaviors, and improve the level of intelligent protection. This paper reviews the current situation and future trends from four aspects: network security information extraction, knowledge ontology modeling, and graph storage and reasoning, aiming to promote the application of knowledge graph in IoT security.

关 键 词：威胁情报 知识图谱 实体识别 关系抽取 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]