## 3 Related Work
Finally, the CICIDS 2018 dataset is not relevant to the IoT problems since it is not derived from pure IoT traffic. The authors of ([PERSON] et al., 2019) made use of the IoT-23 dataset by choosing a broad variety of Machine Learning methods. They compared the performance of the Random Forest (RF) algorithm to the Naive Bayes (NB) algorithm, the Multi-layer Perception (MLP) algorithm, the Support Vector Machine (SVM), and AdaBoost (ADA). The Random Forest approach has the highest accuracy (99.5%) compared to the other methods. Our results were consistent with those of the mentioned study as well as those of [PERSON] et al. (2019). Different IoT security issues examined in ([PERSON] et al., 2019) included authentication, access control, safe offloading, and virus detection. They also researched different machine learning approaches in detail, including supervised, unsupervised, and Reinforcement Learning (RL), with the goal of using such algorithms on the constrained hardware of IoT devices. As part of ([PERSON] et al., 2019), a survey was conducted to assess the current use of IoT-ML in the medical field. According to research, the sensing layer is the weakest link on the Internet of Things. However, the authors did not discuss the dataset used by the ML algorithms. Different supervised and unsupervised ML approaches were compared using the CICIDS 2017 dataset in a benchmark study on anomaly detection in ML-based IDS ([PERSON] et al., 2019). ANN, DT, k-NN, NB, RF, SVM, CNN, EM, K-means, and SOM were examined in their benchmark study. The models had trouble spotting multi-classification attacks. A recent review on IDS based on ML and DL algorithms ([PERSON] et al., 2019) used different datasets and found that the combination of the ML and DL with the different datasets produced promising results to achieve the highest accuracy, but the F1 and recall were not on the focus of the study, indicating that it is not a fair evaluation. According to the findings of the extensive study of IoT IDS reported in ([PERSON] et al., 2019), in ([PERSON] et al., 2019), the authors used Hierarchical Clustering Based on Dendrogram, which is a method used in sustainable transportation systems to group similar objects or elements into clusters. This method uses a dendrogram, a tree-like diagram, to represent the hierarchical structure of the clusters. In this approach, the objects are first considered as individual clusters, and then they are merged into bigger clusters based on their similarity. The dendrogram helps to visualize the relationships between the clusters and to determine the optimal number of clusters for the data. This method can be useful for identifying patterns and trends in transportation data, such as traffic flow or public transportation usage, and for making decisions about transportation infrastructure and planning. It is hard to use DL's computing capacity in a constrained environment such as the IoT. However, offloading this capability to the edge or cloud may help mitigate part of the problem. Table 1 summarizes the comparison between the different approaches and datasets used in the literature surveyed in this paper. Based on this survey, Figure 4 compares the different datasets used for the same purpose.