The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

中文翻译：

---

物联网安全中的机器学习：当前的解决方案和未来的挑战

未来的物联网 (IoT) 将对我们的生活产生深远的经济、商业和社会影响。物联网网络中的参与节点通常是资源受限的，这使得它们成为网络攻击的目标。在这方面，主要通过传统加密方法解决物联网网络中的安全和隐私问题已经做出了广泛的努力。然而，物联网节点的独特特性使得现有的解决方案不足以涵盖物联网网络的整个安全范围。机器学习 (ML) 和深度学习 (DL) 技术能够在物联网设备和网络中提供嵌入式智能，可以用来应对不同的安全问题。在本文中，我们系统地回顾了安全要求、攻击向量、以及当前物联网网络的安全解决方案。然后，我们阐明了这些安全解决方案中需要 ML 和 DL 方法的差距。最后，我们详细讨论了用于解决物联网网络中不同安全问题的现有 ML 和 DL 解决方案。我们还讨论了基于 ML 和 DL 的物联网安全的几个未来研究方向。