The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

中文翻译：

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物联网 (IoT) 安全的机器和深度学习方法调查

物联网 (IoT) 集成了数十亿个智能设备，这些设备可以在最少的人工干预下相互通信。物联网是计算史上发展最快的领域之一，到 2020 年底估计有 500 亿台设备。 然而，物联网系统的交叉性质以及此类系统部署涉及的多学科组件带来了新的安全挑战. 为物联网设备及其固有漏洞实施加密、身份验证、访问控制、网络和应用程序安全等安全措施是无效的。因此，应加强现有的安全方法，以有效保护物联网生态系统。机器学习和深度学习 (ML/DL) 在过去几年取得了长足的进步，在几个重要的应用中，机器智能已经从实验室的新颖性转变为实用的机器。因此，ML/DL 方法对于将物联网系统的安全性从仅仅促进设备之间的安全通信转变为基于安全的智能系统非常重要。这项工作的目标是对 ML 方法和可用于开发物联网系统增强安全方法的 DL 方法的最新进展进行全面调查。介绍了与固有或新引入的威胁相关的物联网安全威胁，并讨论了各种潜在的物联网系统攻击面以及与每个面相关的可能威胁。然后，我们彻底审查了物联网安全的 ML/DL 方法，并介绍了每种方法的机会、优点和缺点。我们讨论了将 ML/DL 应用于物联网安全所涉及的机遇和挑战。这些机遇和挑战可以作为未来潜在的研究方向。