# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 2 Related Work
The study performance was tested using the honeypot Cowrie dataset, with various cyberattacks, with an accuracy of 97.4% using the SVM classifier. The effectiveness of the PCA and the results obtained without it were compared by the authors in [35]. Prior to being used in various ML-based techniques, the dataset was first submitted to Principal Component Analysis (PCA) for feature selection. This experimental investigation demonstrates that utilizing PCA reduces algorithm execution time greatly, with a smaller number of features, while producing the same results as not using PCA. In addition, when compared to SVM, the DT and RF algorithms accurately classified DDoS packets. Matplotlib was used to create a graph to display the results. The IoT-23 dataset was used for our experimental analysis. The authors in [36] developed an IDS model based on a hybrid AI model for the classification of attacks for an IoT-based system. The CIC-IDS2017 and UNSW-NB15 datasets were used to evaluate the performance of the suggested model. The model fared better, with a detection rate of 99.75% and an accuracy of 99.45%. The authors of [6] presented a hybrid rule-based feature selection DL-based IDS paradigm for IIoT to train and validate data extracted from TCP/IP packets. A hybrid rule-based feature selection and deep feedforward neural network model were used to implement the training procedure. NSL-KDD and UNSW-NB15, two well-known network datasets, were used to test the suggested approach. According to the findings of the performance comparison, the suggested strategy outperforms other pertinent methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and by 98.9%, 99.9%, and 1.1%, for the UNSW-NB15 dataset. The recommended method is suitable for IIOT intrusion network attack classification, according to simulated trials utilizing a variety of assessment metrics. The authors of [37] proposed the RDTIDS intrusion detection system (IDS) for IoT networks. The RDTIDS integrates multiple classifier methodologies, such as REP Tree, JRip algorithm, and Forest PA, which are based on decision tree and rules-based principles. The first and second methods specifically classify the network traffic as attack/benign by using features from the data set as inputs. The outputs of the first and second classifiers are used as inputs for the third classifier, together with characteristics from the initial data set. The extensive experiments demonstrate the proposed IDS' effectiveness over existing state-of-the-art schemes in terms of accuracy, detection rate, false alarm rate, and time overhead. These findings were made using the CICIDS2017 dataset and the BoT-IoT dataset. Authors in [38] suggested a novel ensemble of Hybrid IDSs for IoT device security by fusing a C5 classifier and a One-Class Support Vector Machine classifier. The benefits of Signature IDS and Anomaly-based IDS are combined in HIDS. With high detection accuracy and low false-alarm rates, this system seeks to identify both known intrusions and zero-day threats. The Bot-IoT dataset, which includes legal IoT network traffic and various assaults, is used to assess the proposed HIDS. Studies reveal that, compared to SIDS and AIDS approaches, the proposed hybrid IDS offers a higher detection rate and a reduced percentage of false positives. To identify out-of-norm actions for cyber threat hunting in the IIoT, the authors of [39] presented an ensemble DL-based model that combines LSTM with the Auto-Encoder (AE) architecture. Additionally, most of the prior literature did not consider the uneven nature of IIoT datasets, which led to low accuracy and performance. The suggested approach takes fresh, balanced data from the unbalanced datasets and feeds these new balanced data into the deep LSTM AE anomaly detection model to resolve this issue.