中圖分類號：TN918.91；TP309文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2025.05.003
引用格式：吳寶江. 基于數(shù)據(jù)驅動的網(wǎng)絡安全態(tài)勢感知預測［J］.網(wǎng)絡安全與數(shù)據(jù)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