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.

翻译：随着物联网设备、应用及交互日益网络化与集成化，针对物联网的攻击事件持续增加。针对物联网网络的网络攻击激增，对关键系统的隐私性、安全性、功能性与可用性构成了巨大的脆弱性与威胁，进而导致运营中断、财务损失、身份盗窃及数据泄露。为有效保护物联网设备，实时入侵检测系统至关重要，尤其是那些利用机器学习识别威胁并降低风险与脆弱性的系统。本文研究了基于机器学习的物联网安全入侵检测策略的最新进展，重点关注实时响应能力、检测准确率与算法效率。本研究从所有知名学术数据库中梳理了关键文献，并对现有方法进行了分类归纳。本综述同时指出现有研究空白，阐明当前物联网安全框架的局限性，从而为未来研究方向与发展提供实践启示。