In the age of the Internet, people's lives are increasingly dependent on today's network technology. However, network technology is a double-edged sword, bringing convenience to people but also posing many security challenges. Maintaining network security 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. In the field of network information security, the technical update of network attack and network protection is spiraling. 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 threats 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 cybersecurity resilienc e in the financial industry. Many network security researchers have shifted their focus to exceptio n-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.). In the detection phase, program code or network behavior that deviates from the normal value beyond the tolerance is considered malicious code or network attack behavior.

翻译：在互联网时代，人们的生活日益依赖于当前的网络技术。然而，网络技术是一把双刃剑，在为人们带来便利的同时，也带来了诸多安全挑战。维护网络安全、保护用户合法权益是网络建设的核心。威胁检测是构建完整有效防御体系的重要环节。在网络信息安全领域，网络攻击与网络防护的技术更新呈螺旋式上升态势。如何有效检测未知威胁是网络防护的重点关注问题之一。目前，网络威胁检测通常基于规则和传统机器学习方法，这些方法需人为制定规则或提取通用时空特征，难以适用于大规模数据应用，且未知威胁的出现会导致原有模型检测精度下降。基于此，本文利用深度学习进行高级威胁检测，以提升金融行业的网络安全韧性。众多网络安全研究人员已将研究重点转向基于异常的入侵检测技术。该检测技术主要采用统计机器学习方法，即收集正常的程序与网络行为数据，提取多维特征，并据此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）。在检测阶段，偏离正常值超出容限范围的程序代码或网络行为，将被认定为恶意代码或网络攻击行为。