利用深度学习增强金融网络弹性
Utilizing Deep Learning for Enhancing Network Resilience in Finance
AI总结：
针对传统规则与机器学习方法在大规模数据及未知风险下检测准确率下降的问题，本文提出利用深度学习进行高级威胁检测，以增强金融行业的网络防护措施。
摘要：
在互联网时代，人们的生活日益依赖当今的网络技术。维护网络完整性与保护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系统的重要组成部分。如何有效检测未知威胁是网络防护的关注点之一。当前，网络威胁检测通常基于规则和传统机器学习方法，这些方法制定人工规则或提取常见时空特征，无法应用于大规模数据场景，且未知风险的出现导致原模型检测准确率下降。鉴于此，本文采用深度学习进行高级威胁检测，以改善金融业的防护措施。许多网络研究人员已将焦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——收集正常程序与网络行为数据，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）。
英文摘要：
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.).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
