With the development of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have emerged to wireless communication system, especially in cybersecurity. In this paper, we offer a review on attack detection methods involving strength of deep learning techniques. Specifically, we firstly summarize fundamental problems of network security and attack detection and introduce several successful related applications using deep learning structure. On the basis of categorization on deep learning methods, we pay special attention to attack detection methods built on different kinds of architectures, such as autoencoders, generative adversarial network, recurrent neural network, and convolutional neural network. Afterwards, we present some benchmark datasets with descriptions and compare the performance of representing approaches to show the current working state of attack detection methods with deep learning structures. Finally, we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.

中文翻译：

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基于深度学习技术的网络攻击检测方法研究

随着第五代网络和人工智能技术的发展，无线通信系统出现了新的威胁和挑战，特别是在网络安全方面。在本文中，我们对涉及深度学习技术优势的攻击检测方法进行了综述。具体来说，我们首先总结网络安全和攻击检测的基本问题，并使用深度学习结构介绍一些成功的相关应用程序。在对深度学习方法进行分类的基础上，我们会特别关注基于不同体系结构（例如自动编码器，生成对抗网络，递归神经网络和卷积神经网络）的攻击检测方法。之后，我们提供了一些具有描述的基准数据集，并比较了代表方法的性能，以显示具有深度学习结构的攻击检测方法的当前工作状态。最后，我们总结了本文，并讨论了在利用深度学习结构的思想下提高攻击检测性能的一些方法。