## 2 Related Work  
The emerging use of IoT networks and their relative novelty, along with their security threats, has led to the recent endeavour of building specialised IoT datasets. This section presents some of the most relevant published public datasets, which were developed specifically in IoT sensor networks with sufficient traces and are perfectly labelled, designed for IDSs using supervised machine learning. As well as this, examples of their implemention for traffic anomaly detection using machine learning are shown. * N-BaIoT [9]: This dataset is composed of captured traffic from two networks, the first one corresponding to a video surveillance network with internet protocol (IP) cameras with eight different types of attacks that interrupt the availability and integrity of the video links. The second one comes from an IoT network with three computers and nine IoT nodes, one of which is affected with the Mirai botnet malware. This dataset was employed to detect botnet attacks in the IoT by extracting snapshots of network behaviour and using deep autoencoders. In addition, using the mentioned dataset, [PERSON] [10] devised a system for network intrusion detection, which learns how to detect online attacks on local networks without supervision using autoencoders. Furthermore, [PERSON] [11] exploited the dataset for classification using logistic regression (LR) and artificial neural networks (ANN). * CCD-INID-V1: [PERSON] et al. [12] created a new dataset to identify traffic anomalies in IoT networks. In addition, they developed a hybrid intrusion detection method that incorporated an embedded model for feature selection and a convolutional neural network (CNN) for attack classification. The considered method has two models: RCNN, where random forest is combined with a CNN, and XCNN, where extreme gradient boosting (XGBoost) is combined with a CNN. They compared the accuracy of their method with classical ML techniques, such as support vector machine (SVM), K-nearest neighbours (KNN), LR, and naive Bayes (NB). To validate their experiments, they also compared their proposed dataset with the N_BaIoT dataset [9] and the DoH20 dataset [13] to investigate the performance of these learning-based security models and compare their method against conventional ML techniques. * [PERSON] et al. : They developed some classifiers to automatically identify attacks in IoT traffic. They demonstrated that considering IoT traffic information in the feature selection process can lead to a high accuracy distributed denial of service (DDoS) detection [14]. A pipeline was assembled to operate on network middleboxes and corresponding devices that may be part of an ongoing botnet. Different classifiers for attack detection were implemented, such as KNN, support vector machine with linear kernel (LSVM), decision tree (DT), random forest (RF) and neural networks (NN). * All eyes on you: [PERSON] et al. [15] developed an IoT microservice anomaly detection, where they created and published two separate datasets. The datasets monitored connections between seven different virtual state layer (VSL) service types of light controllers, movement sensors, thermostats, solar batteries, washing machines, door locks, and user smartphones. Additionally, employing this dataset, [PERSON] et al. [16] proposed an ML system using LR, SVM, DT, RF, and ANN. The proposed models based on ML could recognise and conserve the system when it is in an abnormal state. In training and test stages, the best accuracy results were obtained with RF and ANN..
* [PERSON] et al. : They presented a dataset based on eight commercially popular IoT nodes as an example of a smart home [17]. They collected benign data for three weeks and malicious data for two weeks from the IoT testbed in five scenarios with six IoT attack categories. They also proposed a three-layer IDS considering NB, Bayesian networks, J48, Zero R, OneR, simple logistic, SVM, multi-layer perceptron (MLP), and RF, generating potential intrusion detection systems.