Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a huge vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.

本研究针对物联网安全性面临的日益增加的网络攻击，探讨了机器学习在实时入侵检测中的应用。通过回顾最新的研究成果，提出了有效的检测策略，并指出现有安全框架的研究空白与局限，为未来研究提供了实践性见解。

机器学习辅助的入侵检测以增强物联网安全性