The constant spread of smart devices in many aspects of our daily life goes hand in hand with the ever-increasing demand for appropriate mechanisms to ensure they are resistant against various types of threats and attacks in the Internet of Things (IoT) environment. In this context, Deep Learning (DL) is emerging as one of the most successful and suitable techniques to be applied to different IoT security aspects.

This work aims at systematically reviewing and analyzing the research landscape about DL approaches applied to different IoT security scenarios. The contributions we reviewed are classified according to different points of view into a coherent and structured taxonomy in order to identify the gap in this pivotal research area.

The research focused on articles related to the keywords ’deep learning’, ’security’ and ’Internet of Things’ or ’IoT’ in four major databases, namely IEEEXplore, ScienceDirect, SpringerLink, and the ACM Digital Library.

We selected and reviewed 69 articles in the end. We have characterized these studies according to three main research questions, namely, the involved security aspects, the used DL network architectures, and the engaged datasets. A final discussion highlights the research gaps still to be investigated as well as the drawbacks and vulnerabilities of the DL approaches in the IoT security scenario.

智能设备在我们日常生活的各个方面的不断发展与对适当机制的不断增长的需求密切相关，以确保它们能够抵御物联网（IoT）环境中的各种类型的威胁和攻击。在这种情况下，深度学习（DL）成为一种最成功，最合适的技术，可应用于不同的IoT安全方面。

这项工作旨在系统地审查和分析有关应用于不同物联网安全场景的DL方法的研究前景。根据不同的观点，我们审查的文稿被分类为一个连贯的，结构化的分类法，以找出这一关键研究领域中的空白。

该研究的重点是与四个主要数据库（即IEEEXplore，ScienceDirect，SpringerLink和ACM数字图书馆）中的“深度学习”，“安全”和“物联网”或“ IoT”相关的文章。

最后，我们选择并审查了69篇文章。我们已经根据三个主要研究问题对这些研究进行了特征化，即涉及的安全方面，使用的DL网络体系结构和参与的数据集。最后的讨论着重指出了尚待研究的研究差距，以及物联网安全场景中DL方法的弊端和脆弱性。