## 2 The State-of-the-Art
As a result of the conducted experiments, the authors of the study concluded that the most acceptable classification accuracy and the time of response were provided by the classification trees, regression trees, and extreme gradient boosting. An approach for cyberattack detection as an AD-IoT system is presented in [21]. The proposed system is designed for the smart city infrastructure and is based on the random forest machine learning algorithm. The system aims to detect the compromised IoT devices that are placed in the distributed fog nodes. The division of normal and malicious behaviors of IoT devices is executed on the basis of monitoring and analyzing the fog nodes' network traffic. Such analysis is performed to verify whether the fog level attacks are detected and to inform the cloud security services concerning the evaluated results. The presented approach demonstrates sufficient detection efficiency and applies to the smart city infrastructure. An approach for DDoS attack detection is presented in [22]. It is based on the hybrid optimization algorithms of Metaheuristic lion and Firefly. It was designed to perform data collecting, data preprocessing for noise removing, and filling missing data. The feature extraction was performed by employing recursive feature elimination (RFE). An important item of the proposed technique is the possibility of detecting low-rate attacks using the hybrid ML-F optimization algorithm. For the attack classification, a random forest classifier was used. The article [23] introduces an IDS, which is based on the technique that uses an ensemble-based voting classifier. This approach uses multiple classifiers as a base learner. The final prediction is formed via producing the classifier's vote for the traditional classifier predictions. As the mean of the efficiency evaluation of the presented approach, a set of IoT devices with the usage of different sensors (garage door, light motion, GPS sensor, fridge sensor, thermostat, modbus, and weather) were employed. Multi-class attacks, such as XSS, Ransomware, scanning injection, DDoS, and backdoor, were involved in the technique efficiency verification. The efficiency of the presented method was compared with the set of new intrusion detection approaches provided by scientists. The comparison was constructed on the basis of the accuracy, precision, recall, and F-score metrics. Furthermore, a set of machine learning algorithms, such as decision tree, naive Bayes, random forest, and K-nearest neighbors were involved in the comparison procedure. The experimental results demonstrated that the proposed approach has a high detection efficiency. The authors of [24] propose a detection method for DoS/DDoS attacks against the IoT using machine learning. The approach aims to detect and apply the mitigation scenarios in the situation of DoS/DDoS attacks. To do this, the approach employs a multiclass classifier (\"Looking back\"). In addition, the ability of the technique to detect \"malicious\" packets makes it possible to apply mitigation measures against attacks that employ specific packet types. The approach in [25] provides a botnet detection system for IoT devices. It is based on the algorithm named local-global best bat, which is used for neural networks and is able to process the botnet's feature sets to distinguish malicious and benign network traffic. As an experimental part of the study, the botnets Mirai and Gafgyt were used to infect several commercial IoT devices. In addition, to classify 10 botnet classes, the proposed algorithm was used. It was designed to tune the neural network hyperparameters and optimize the weight. The authors made the efficiency comparison of the provided algorithm with other approaches. The experimental results demonstrated that the proposed botnet detection approach accuracy was up to 90%, while BA-NN was 85.5%, and PSO-NN was 85.2%.