###### Abstract
The authors also proposed an architecture based on LSTM to classify network assaults in IoT networks efficiently using several classes. The assessment of the suggested model demonstrated its efficacy in accurately detecting assaults and categorizing them, with a classification accuracy of 0.971. Furthermore, various visualization techniques were used to get insights into the dataset's properties and visually represent the embedding features. Identifying and differentiating such threats pose significant challenges, necessitating a sophisticated IDS. ML has emerged as a promising methodology for developing intelligent IDSs across several domains, including the IoT. Nevertheless, it is crucial to note that the input for ML models must be derived from the IoT environment via feature extraction models. These models have considerable importance in determining the detection rate and accuracy of the ML models. Hence, the primary objective of study [12] was to investigate the implementation of machine learning-based IDSs in the IoT. The investigation specifically focused on evaluating several feature extraction methods with many machine learning models. That work evaluated several feature extractors, including image filters, and transfer learning models such as VGG-16 and DenseNet. Furthermore, considering all the feature extraction approaches studied, a comprehensive evaluation was conducted on several machine learning techniques, such as random forest, K-nearest neighbors, support vector machine (SVM), and different stacked models. The research comprehensively assessed the collective models using the IEEE Dataport dataset. The study's findings revealed that utilizing VGG-16 with stacking techniques yielded the most noteworthy accuracy rate, reaching 98.3%. An advanced approach to enhancing the security of the IoT involves using deep learning techniques. This approach presents a coherent solution for anomaly-based detection. The research by the authors of [13] introduced a convolutional neural network (CNN) technique for anomaly-based IDSs in the field of IoT. The proposed approach leveraged the capabilities of IoT to analyze the whole network traffic inside the IoT ecosystem effectively. The model under consideration could identify and classify instances of intrusion and anomalous patterns in network traffic. The model underwent training and testing procedures using the NID Dataset and BoT-IoT datasets, yielding accuracy rates of 99.51% and 92.85%, respectively. The authors of [14] developed a novel framework based on Explainable Artificial Intelligence (XAI) to detect intrusions in IoT networks. The proposed framework incorporates a deep neural network model as the first component for real-time intrusion detection. Once the model has been determined, their framework incorporated three distinct ways of Explainable Artificial Intelligence (XAI) to enhance the model's decision-making process with increased levels of explainability, transparency, and trust. Furthermore, the framework was designed to cater to two distinct user groups: users of the deep learning model who seek to comprehend and have confidence in the model's outputs to enhance their decision-making, and cybersecurity experts who also desire to comprehend the model's outputs to provide appropriate recommendations, particularly in the event of an intrusion being detected. The feasibility and performance of the framework were demonstrated using the NSL-KDD and UNSWNB15 datasets. The experimental results indicated the effectiveness of the proposed XAI-based framework in detecting attacks in IoT systems. Furthermore, their framework provided additional insights and explanations regarding the deep neural network model's decision-making process, enhancing the interpretation of the detection outcomes. The researchers' findings indicated that the XAI framework yielded 88% and 99% accuracy when applied to the NSL\(-\)KDDTest and UNSW-NB15 datasets.