# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 2 Related Work
They employed various ML-based models, with XGBoost outperforming all others on all datasets according to the numerous performance criteria they used to assess the suggested models. In [25], the authors applied supervised and unsupervised ML over the NF-ToN-IoT-v2 dataset to provide a thorough model of a network IDS (NIDS). It was demonstrated that the technique XGBoost Classifier, which obtained a F-Score of 98.8%, produced the best results when supervised learning was used, as implemented by Azure automated ML (AML). The random forest classifier, with a F-Score of 98.6%, produced the greatest results when a specially designed automated ML (AE2 EML) was used. The suggested ML-based NIDS obtained a Silhouette score of 0.553, a Calinski-Harabasz index of 1533106, and a Davies-Bouldin index of 0.631 using clustering with PCA (Principal Component Analysis), performed by PyCaret-automated ML. The proposed model by the study performed excellently, but it used old datasets that did not contain recent IIoT-based network attacks. By examining the applicability of ML-based algorithms in the detection of abnormalities within the data of such networks, the authors of [26] concentrated on the security element of IoT networks. It investigates ML algorithms that have been effectively applied in circumstances that are comparable to one another and contrasts them using a variety of factors and techniques. The RF algorithm produced the best results, with a 99.5% accuracy rate. The authors of [27] presented an IDS with an ensemble classifier enabled by a feature selection classifier. The study utilized the Correlation Coefficient (CC) method for feature selection before classifying the dataset for the detection of various attacks using various classifiers, such as NB, DT, and ANN. On the UNSW-NB 15 datasets, the system detected DoS assaults with an accuracy of 98.54% with a classifier ensemble that uses a subset of the features. The dataset used to test the model is not an IIoT-based network nodes attacks dataset and does not employ feature selection methods to remove the irrelevant features from the dataset used, and the issue of imbalance data is not considered, thus reducing the performance of the model. The authors in [28] used the top 13 IG characteristics with the C5 classifier to obtain improved accuracy of 89.76% and a better FAR of 1.68. The study recommended that IG be used for choosing features for IDS. The top ten ranked IG attributes were used in the system to create a greater accuracy of 93.23% with 6.77% FAR. The authors obtained six reduced features in [29] using the multi-objective feature selection method on the CICIDS 2017 dataset. The system delivered an accuracy of 99.90% using an ELM classifier. To detect cyberattacks, the study authors in [30] suggested using LSTM networks enabled with parameter optimization, called Stochastic Gradient Descent (SGD), for the creation of IDS. The study obtained an accuracy of 99.91% for ISCX and 98.22% for AWID datasets, respectively. The top 10 attributes of the GR technique were used in work by authors in [31], and their layer design was validated on a generated dataset. In contrast to previous rules and tree-based learners, the design performed better with the J48 classifier for recognizing DoS assaults. A decision tree-based multi-layer framework to identify DDoS attacks was provided in the study of authors in [32]. The system recognized ICMP, TCP, and UDP flood attacks on a created dataset, with an accuracy of 99.98%, using eight features that were explicitly picked. The authors in [33] utilized nature-inspired techniques for feature selection with forecasting and chaos methods. The performance of the model was evaluated using the NS-3 created model. For the identification of DoS assaults at the transport and application layers, the approach obtained a detection rate (DR) of 94.3%. The authors employed the wrapper feature selection approach in [34] for feature selection in IDS.