In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is an important part of a complete and effective defense system. How to effectively detect unknown threats is one of the concerns of network protection. Currently, network threat detection is usually based on rules and traditional machine learning methods, which create artificial rules or extract common spatiotemporal features, which cannot be applied to large-scale data applications, and the emergence of unknown risks causes the detection accuracy of the original model to decline. With this in mind, this paper uses deep learning for advanced threat detection to improve protective measures in the financial industry. Many network researchers have shifted their focus to exception-based intrusion detection techniques. The detection technology mainly uses statistical machine learning methods - collecting normal program and network behavior data, extracting multidimensional features, and training decision machine learning models on this basis (commonly used include naive Bayes, decision trees, support vector machines, random forests, etc.).

翻译：在互联网时代，人们的生活日益依赖现代网络技术。维护网络完整性并保障用户合法权益是网络建设的核心。威胁检测是完整有效防御体系的重要组成部分。如何有效检测未知威胁是网络防护的关键问题之一。当前网络威胁检测通常基于规则和传统机器学习方法，这些方法通过人工制定规则或提取通用时空特征实现，难以适用于大规模数据应用，且未知风险的出现会导致原始模型检测精度下降。基于此，本文采用深度学习进行高级威胁检测，以改进金融行业的防护措施。众多网络研究者已将研究重心转向基于异常的入侵检测技术。该检测技术主要运用统计机器学习方法——收集正常程序及网络行为数据，提取多维特征，并在此基础上训练决策机器学习模型（常用方法包括朴素贝叶斯、决策树、支持向量机、随机森林等）。