## 2 The State-of-the-Art  
The scientific community is focusing on the increasing problems concerning cybersecurity today. Solutions devoted to cyberattack detection against Internet of Things infrastructure are widely presented [7; 8]. Quite possibly, the most encouraging approaches for IoT cyberattack detection are based on machine learning algorithms (MLA) [9; 10; 11; 12; 13]. To solve the cyberattack detection problem, the authors of [14] proposed an approach that executes the IoT malware traffic analysis. It is based on the usage of multilevel artificial intelligence and involves neural networks and binary visualization. In addition, the approach proposes efficiency improvement via learning from the misclassification approach, which includes three main stages, is designed to collect the network traffic, perform the binary visualization to store the collected network traffic in ASCII, convert it to 2D images, and process/analyze the obtained binary image. An analysis of the binary images is executed using the TensorFlow tool, an end-to-end open-source platform designed to use machine learning for different problem solutions. It can find and classify patterns automatically. The main advantage of the tool is the ability to organize the system retraining as well as the possibility to make the image recognition. The approach proposes the use of the algorithm to perform the visualization of the collected traffic characteristics as an image (in the form of tiles using the Binvis tool). The TensorFlow machine tool can make predictions. The use of graphic tiles allows the determination of the tile combination on which the image is based. It is able to detect needed objects regardless of the location within the obtained image. The provided method can perform the IoT device protection on the gateway level, bypassing the IoT environment constraints. The authors of [15] presented a survey on the experimental studies with a detailed analysis of a set of machine learning algorithms. The article included comparative data concerning the algorithm detection efficiency of anomalous behavior in IoT networks. Experimental results have shown that the best efficiency concerning used datasets is produced by the random forest algorithm. Nevertheless, all investigated machine learning algorithms demonstrated to be very close to random forest algorithm and detection efficiency results; sometimes the choice of an appropriate algorithm depends on the nature of the analyzed data. Article [16] is devoted to machine learning classifiers involved in the botnet traffic analysis in the IoT environment. Nine IoT devices were employed for dataset construction, consisting of several botnet attack types. To evaluate the efficiency of the proposed approach, true positive, true negative, false positive, false negative, F1-score accuracy, precision, and recall were used. The experimental results of the research demonstrated thatthe random forest algorithm produced the best results while the support vector machine produced the lowest results. The main disadvantage of the approach is the strong need for data analysis of all features in processed datasets. The IoT cyberattack detection approach for the IoT network is presented in [17]. It is based on the use of intelligent technologies. The produced intelligent system operates with a set of network features. The approach aims to reduce the feature number via its ranking with the usage of the correlation coefficient, random forest algorithm, and the gain ratio. The base for the experimental research involves three feature sets, where using the proposed algorithm is to be combined to obtain an optimized feature set. The means of data processing the authors used were K-nearest neighbor, random forest, and XGBoost machine learning algorithms. All experiments were based on the usage of NSL-KDD, BoT-IoT, and DS2 OS datasets. The investigation of the detection efficiency of the proposed system was executed. For this purpose, the metrics of accuracy, detection rate, F1-score, and precision were evaluated.