Abstract: In the face of increasingly complex network security attacks, it is very important to quickly obtain the latest network threat intelligence for realtime identification, blocking and tracking of network attacks. The key to solve this problem is how to obtain network threat intelligence data effectively, and named entity recognition technology is one of the hot technologies to solving this problem. This paper systematically analyzes several named entity recognition methods based on deep learning, and then designs a named entity recognition model suitable for threat intelligence field, and carries out experimental verification and analysis. Finally, the challenges faced by named entity recognition methods and their development prospects in the field of network security are analyzed and prospected.

Key words: network security attacks, threat intelligence, named entity recognition, deep learning, challenge analysis

摘要： 面对日益复杂多变的网络安全攻击，迅速获取最新的网络威胁情报对于实时识别、阻断和追踪网络攻击至关重要.解决这一问题的关键在于如何有效地获取网络威胁情报数据，而命名实体识别技术是解决这一问题的热点技术之一.系统分析了多种基于深度学习的命名实体识别方法，而后设计了一种适用于威胁情报领域的命名实体识别模型，并进行了实验验证和分析.最后对命名实体识别方法面临的挑战及其在网络安全领域的发展前景进行了分析和展望.

关键词: 网络安全攻击, 威胁情报, 命名实体识别, 深度学习, 挑战分析