The increase in network attacks has necessitated the development of robust and efficient intrusion detection systems (IDS) capable of identifying malicious activities in real-time. In the last five years, deep learning algorithms have emerged as powerful tools in this domain, offering enhanced detection capabilities compared to traditional methods. This review paper studies recent advancements in the application of deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), autoencoders (AE), Multi-Layer Perceptrons (MLP), Self-Normalizing Networks (SNN) and hybrid models, within network intrusion detection systems. we delve into the unique architectures, training models, and classification methodologies tailored for network traffic analysis and anomaly detection. Furthermore, we analyze the strengths and limitations of each deep learning approach in terms of detection accuracy, computational efficiency, scalability, and adaptability to evolving threats. Additionally, this paper highlights prominent datasets and benchmarking frameworks commonly utilized for evaluating the performance of deep learning-based IDS. This review will provide researchers and industry practitioners with valuable insights into the state-of-the-art deep learning algorithms for enhancing the security framework of network environments through intrusion detection.

翻译：网络攻击的增加促使人们开发能够实时识别恶意活动的稳健高效的入侵检测系统（IDS）。近五年来，深度学习算法已成为该领域的强大工具，与传统方法相比，其检测能力显著增强。本综述论文研究了深度学习技术在网络入侵检测系统中的最新应用进展，包括卷积神经网络（CNN）、循环神经网络（RNN）、深度信念网络（DBN）、深度神经网络（DNN）、长短期记忆网络（LSTM）、自编码器（AE）、多层感知机（MLP）、自归一化网络（SNN）以及混合模型。我们深入探讨了专为网络流量分析和异常检测设计的独特架构、训练模型及分类方法。此外，我们从检测精度、计算效率、可扩展性以及适应不断演变的威胁的能力等方面，分析了每种深度学习方法的优势与局限性。同时，本文还重点介绍了常用于评估基于深度学习的入侵检测系统性能的突出数据集和基准测试框架。本综述将为研究人员和行业从业者提供关于最先进的深度学习算法的宝贵见解，这些算法通过入侵检测增强网络环境的安全框架。