## 2 Related Works  
In this section, we review research on security within Internet of Things (IoT) and industrial Internet of Things (IIoT) networks. [PERSON] et al. [7] introduced the Edge-IIoTset dataset, specifically designed for machine-learning-based intrusion detection systems. They also analyzed fourteen attack types relevant to IoT and IIoT networks, categorizing them into five distinct attack groups. [PERSON] et al. [8] examined the primary cybersecurity challenges faced by IoT systems, utilizing a simplified threat model based on the STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) method to investigate network security issues. Additionally, [PERSON] et al. [9] contributed to the Industry 4.0 Cyber Security Project by proposing effective strategies to secure Machine-to-Machine (M2M) communications in industrial IoT networks.  
[PERSON] et al. [10] utilized a genetic algorithm to develop an intrusion detection system specifically for industrial IoT networks. Their approach incorporated a random forest (RF) model within the fitness function of the genetic algorithm. The proposed model was tested for intrusion detection across various classifiers, including RF, Linear Regression (LR), Naive Bayes (NB), decision tree (DT), Extra-Trees (ET), and Extreme Gradient Boosting (XGB). [PERSON] et al. [11] employed a deep-learning-based IDS technique for industrial IoT networks, presenting different methods for intrusion detection and performing a comparative analysis on the dataset. [PERSON] et al. [12] investigated intrusion detection using deep learning and proposed a Sparse Evolutionary Training (SET)-based prediction model. They thoroughly evaluated their model's performance using metrics such as accuracy, precision, recall, and F1 score, comparing it with other state-of-the-art algorithms.  
[PERSON] et al. [13] explored detection methods and IDS architectures, proposing a hybrid IDS architecture combined with a machine-learning-based intrusion detection approach. [PERSON] et al. [14] introduced a forensics-driven deep learning (DL) model for intrusion detection in IIoT networks. Their model incorporated a local gated recurrent unit and added an MHA layer to capture and learn key features. [PERSON] et al. [15] presented an XGBoost model designed for IDSs in IIoT networks, specifically addressing unbalanced datasets. They assessed the effectiveness and robustness of their method using two unbalanced datasets, with the XGBoost model achieving impressive attack detection results, yielding F1 scores of 99.9% and 99.87% across both datasets, demonstrating its strong performance in intrusion detection for unbalanced multiclass datasets in IIoT networks.  
[PERSON] et al. [16] introduced an intelligent detection system for identifying cyberattacks in IIoT networks, utilizing Singular Value Decomposition (SVD) to reduce data dimensionality and improve recognition accuracy. They also employed the Synthetic Minority Oversampling Technique (SMOTE) to tackle overfitting and underfitting issues, thereby minimizing biased classifications. Additionally, they evaluated several machine learning and deep learning algorithms for both binary and multiclass classification tasks. [PERSON] et al. [17] developed an intrusion detection approach to strengthen IIoT security through machine learning. Their work focused on enhancing feature selection and dimensionality reduction techniques to improve recognition rates and model accuracy. To reduce computational cost and time complexity in high-dimensional datasets, they recommended using Pearson's correlation coefficient (PCC) for feature selection and Isolation Forest (IF) to remove outliers. [PERSON] et al. [18] proposed three distinct models utilizing convolutional neural networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN + LSTM architecture to create an intrusion detection system for IIoT networks.