The widespread adoption of the Internet of Things (IoT) has raised a new challenge for developers since it is prone to known and unknown cyberattacks due to its heterogeneity, flexibility, and close connectivity. To defend against such security breaches, researchers have focused on building sophisticated intrusion detection systems (IDSs) using machine learning (ML) techniques. Although these algorithms notably improve detection performance, they require excessive computing power and resources, which are crucial issues in IoT networks considering the recent trends of decentralized data processing and computing systems. Consequently, many optimization techniques have been incorporated with these ML models. Specifically, a special category of optimizer adopted from the behavior of living creatures and different aspects of natural phenomena, known as metaheuristic algorithms, has been a central focus in recent years and brought about remarkable results. Considering this vital significance, we present a comprehensive and systematic review of various applications of metaheuristics algorithms in developing a machine learning-based IDS, especially for IoT. A significant contribution of this study is the discovery of hidden correlations between these optimization techniques and machine learning models integrated with state-of-the-art IoT-IDSs. In addition, the effectiveness of these metaheuristic algorithms in different applications, such as feature selection, parameter or hyperparameter tuning, and hybrid usages are separately analyzed. Moreover, a taxonomy of existing IoT-IDSs is proposed. Furthermore, we investigate several critical issues related to such integration. Our extensive exploration ends with a discussion of promising optimization algorithms and technologies that can enhance the efficiency of IoT-IDSs.

翻译：物联网的广泛应用为开发者带来了新的挑战，因其异构性、灵活性及紧密连接性而易受已知和未知网络攻击的影响。为防御此类安全漏洞，研究人员致力于利用机器学习技术构建复杂的入侵检测系统。尽管这些算法显著提升了检测性能，但其需要过高的计算能力和资源，考虑到去中心化数据处理与计算系统的最新趋势，这在物联网网络中成为关键问题。因此，许多优化技术已与这些机器学习模型相结合。特别地，一类从生物行为及自然现象不同方面借鉴而来的特殊优化器——即元启发式算法——近年来成为研究焦点并取得了显著成果。鉴于其重要性，本文对元启发式算法在开发基于机器学习的入侵检测系统（尤其是针对物联网）中的多种应用进行了全面而系统的综述。本研究的一个重要贡献在于揭示了这些优化技术与集成于前沿物联网入侵检测系统的机器学习模型之间的隐含关联。此外，本文分别分析了这些元启发式算法在特征选择、参数或超参数调优以及混合使用等不同应用中的有效性。同时，提出了现有物联网入侵检测系统的分类体系。进一步地，我们探讨了与此类集成相关的若干关键问题。通过广泛研究，最后讨论了有望提升物联网入侵检测系统效率的优化算法与技术。