【摘要】 互联网的迅速发展在给用户带来巨大便利的同时，也引发了诸多安全事故.随着零日漏洞、加密攻击等网络攻击行为日益增加，网络安全形势愈发严峻.入侵检测是网络攻击检测的一种重要手段.近年来，随着深度学习技术的持续发展，基于深度学习的入侵检测系统逐渐成为网络安全领域的研究热点.通过对文献的广泛调查，介绍了利用深度学习技术进行网络入侵检测的最新工作.首先，对当前网络安全形势及传统入侵检测技术进行简要概括；然后，介绍了网络入侵检测系统中常用的几种深度学习模型；接着，总结了深度学习中常用的数据预处理技术、数据集以及评价指标；再从实际应用的角度介绍了深度学习模型在网络入侵检测系统中的具体应用；最后，讨论了目前研究过程中面临的问题，提出了未来的发展方向.更多还原

【Abstract】 The rapid development of the Internet not only brings great convenience to users, but also causes many security incidents. With the increasing number of network attacks such as zero-day vulnerabilities and encryption attacks, the network security situation is becoming more and more serious. Intrusion detection is an important means of network attack detection. In recent years, with the continuous development of deep learning technology, intrusion detection system based on deep learning is gradually becoming a research hotspot in the field of network security. This paper introduces recent work on network intrusion detection using deep learning technology based on extensive investigation of literature. Firstly, it briefly summarizes the current network security situation and traditional intrusion detection technologies. Then, several deep learning models commonly used in network intrusion detection system are introduced. Then it summarizes the commonly used data preprocessing techniques, data sets and evaluation indicators in deep learning. Then from the perspective of practical application, it introduces the specific application of deep learning model in network intrusion detection system. Finally, the problems in the current research process are discussed, and the future development direction is put forward.更多还原