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：物联网的广泛普及为开发者带来了新的挑战，由于其具有异构性、灵活性和紧密连接性，容易遭受已知和未知的网络攻击。为应对此类安全漏洞，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，但需要消耗大量的计算能力和资源，考虑到近期数据分散处理与计算系统的趋势，这在物联网网络中是一个关键问题。因此，许多优化技术已被整合到这些机器学习模型中。具体而言，一种从生物习性和自然现象不同方面借鉴而来的特殊优化器类别——称为元启发式算法——近年来已成为焦点，并取得了显著成果。鉴于其重要性，我们针对元启发式算法在开发基于机器学习的入侵检测系统（特别是针对物联网）中的多种应用，进行了全面且系统的综述。本研究的一个重要贡献是发现了这些优化技术与集成先进IoT-IDSs的机器学习模型之间的隐藏关联。此外，我们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应用等不同场景中的有效性。同时，我们提出了现有IoT-IDSs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。我们的广泛探索以讨论有望提升IoT-IDSs效率的优化算法和技术作为结尾。