The integration of the Internet of Things (IoT) connects a number of intelligent devices with a minimum of human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems were posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/ machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT systems protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT Securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach's benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.

翻译：将一些智能装置与能够相互作用的人类干扰最小程度的干扰连接起来(IoT),将一些智能装置整合起来,将一些智能装置与一些可以相互互动的人类干扰连接起来。IoT在计算机科学领域正在迅速出现。然而,由于多学科要素的跨领域设计以及参与部署这种计划的IoT系统,带来了新的安全问题。执行安全协议(即认证、加密、应用安全、互联网系统接入网络及其安全方面的基本弱点)效率低下。目前的安全方法还可以改进,以有效保护IoT环境。近年来,深入学习(DL)/机器学习(ML)在各种关键执行领域取得了显著的进展。因此,DL/ML方法对于使IoT系统能够安全地与安全情报系统进行联系至关重要。这项审查的目的是对ML系统及其当前安全方法的最新发展进行广泛分析,从而改进IoT装置保护方法。另一方面,对于IoSesteic软件的不断出现和深层学习(ML)过程如何帮助对未来的威胁或未来威胁进行认真研究。