An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic response to threats are a few of the problems that intrusion identification is used to solve. The biological system known as IoT has seen a rapid increase in high dimensionality and information traffic. Self-protective mechanisms like intrusion detection systems (IDSs) are essential for defending against a variety of attacks. On the other hand, the functional and physical diversity of IoT IDS systems causes significant issues. These attributes make it troublesome and unrealistic to completely use all IoT elements and properties for IDS self-security. For peculiarity-based IDS, this study proposes and implements a novel component selection and extraction strategy (our strategy). A five-ML algorithm model-based IDS for machine learning-based networks with proper hyperparamater tuning is presented in this paper by examining how the most popular feature selection methods and classifiers are combined, such as K-Nearest Neighbors (KNN) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Gradient Boosting Classifier, and Ada Boost Classifier. The Random Forest (RF) classifier had the highest accuracy of 99.39%. The K-Nearest Neighbor (KNN) classifier exhibited the lowest performance among the evaluated models, achieving an accuracy of 94.84%. This study's models have a significantly higher performance rate than those used in previous studies, indicating that they are more reliable.

翻译：入侵检测系统（IDS）作为一种软件应用，通过机器学习算法识别网络入侵行为。入侵检测旨在解决选择性日志记录、隐私保护、基于信誉的多攻击防御以及动态威胁响应等问题。物联网（IoT）作为一种生物启发的系统，其高维度和信息流量正经历快速增长。以入侵检测系统（IDS）为代表的自保护机制对于防御各类攻击至关重要。然而，物联网IDS系统在功能与物理层面的多样性引发了显著挑战。这些特性使得充分利用所有物联网元素与属性来实现IDS自安全变得困难且不切实际。针对基于异常检测的IDS，本研究提出并实现了一种新颖的特征选择与提取策略（即本方案）。本文通过研究最常用的特征选择方法与分类器的组合——包括K近邻（KNN）分类器、决策树（DT）分类器、随机森林（RF）分类器、梯度提升分类器以及AdaBoost分类器——构建了一种基于五种机器学习算法模型、经过超参数优化的网络入侵检测系统。其中随机森林（RF）分类器取得了99.39%的最高准确率，而K近邻（KNN）分类器在评估模型中表现最低，准确率为94.84%。本研究所构建模型的性能显著优于既有研究，表明其具有更高的可靠性。