摘要： 云计算和互联网的快速发展引发网络数据的爆炸式增长，随之而来的网络威胁也变得日益复杂，大量的数据给网络带来了巨大的安全风险。传统的安全措施往往不足以抵御这些持续动态变化的网络安全威胁，需要综合应用人工智能和机器学习等技术，基于数据驱动形成数据应用智能化，采用态势数据采集、大数据关联分析、安全威胁研判等手段，实现网络安全威胁实时监测并预测潜在的网络攻击行为，支撑防御策略动态调整，提升网络空间安全防御整体效能。此外，基于数据驱动的网络安全态势感知预测系统能够帮助网络安全管理人员丰富网络风险处理相关专业知识，在实际网络安全威胁场景下做出更好的判断和决策。

中图分类号：TN918.91；TP309文献标识码：ADOI:10.19358/j.issn.2097-1788.2025.05.003
引用格式：吴宝江. 基于数据驱动的网络安全态势感知预测［J］.网络安全与数据治理，2025，44（5）：17-20.

Data-driven network security situational awareness and prediction

Abstract： The rapid development of cloud computing and the Internet has led to the explosive growth of network data, and the ensuing network threats have become increasingly complex. A large amount of data has brought huge security risks to the network. Traditional security measures are often insufficient to resist these constantly changing network security threats. It is necessary to comprehensively apply technologies such as artificial intelligence and machine learning to from data-driven intelligent data applications, using situational data collection, big data correlation analysis, security threat analysis and other means to achieve real-time monitoring of network security threats and predict potential network attack behaviors. It can support dynamic adjustment of defense strategies, and improve the overall effectiveness of network security defense. In addition, data-driven security situational awareness and prediction systems can help network security managers enrich their related professional knowledge of network risks, and make better judgments and decisions in actual network security threat scenarios.

Key words : data-driven；security situational awareness and prediction；artificial intelligence；machine learning；security defense