## 2 The State-of-the-Art
The authors of [26] proposed a taxonomy of intrusions detection systems that utilizes the data objects as the dimensions to summarize and classify machine learning- and deep learning-based IDS. The survey clarifies the concept of IDSs. Moreover, machine learning-based algorithms, metrics, and benchmark datasets frequently used in IDSs were introduced. IDSs applied to various data sources, i.e., logs, sessions, packets, and flow, were analyzed. The proposed taxonomic system was presented as a baseline and key IDS issues with using machine learning and deep learning algorithms. Moreover, future developments and challenges of IDS were discussed. The authors of [27] introduced a probabilistic-driven ensemble (PDE)-based approach. This approach operates with several classification algorithms, wherein the effectiveness of these algorithms has been improved by applying a probabilistic criterion. Thus, the proposed approach allows maximizing the possibility of detecting intrusion events, regardless of the operational scenario, using several evaluation models. This makes it possible to distinguish ordinary events from related events to all classes of attacks. Experiments performed by using real-world data show that the proposed ensemble approach has better capability in detecting intrusion events (concerning known solutions). The authors of [28] presented machine learning-based IDS. The feature reduction approach has two components: (1) Auto-encoder as a deep learning instance for dimensionality reduction; and (2) principal component analysis. The resulting set of low-dimensional features from both approaches was used to build different classifiers, i.e., Bayesian network, random forest, linear discriminant analysis, and quadratic discriminant analysis for designing IDS. The obtained experimental findings show better performance in terms of detection rate, false alarm rate, accuracy, and F-measure for binary and multi-class classification. This approach is able to reduce the feature dimensions of the CICIDS2017 dataset from 81 to 10, with high accuracy in both multi-class and binary classifications. The objective of [29] was to apply various approaches for handling imbalanced datasets to design an effective IDS from the CIDDS-001 dataset. The effectiveness of sampling methods based on CIDDS-001 was studied and experimentally evaluated via random forest, deep neural networks, variational autoencoder, voting, and stacking machine learning classifiers. The developed system makes it possible to detect attacks with high accuracy when processing an unbalanced distribution of classes using a smaller number of samples. It makes it possible to apply the proposed system to data classification problems if it is necessary to merge data in real-time. In [30], the authors were devoted to solving cybersecurity problems, such as the difficulty in distinguishing illegitimate activities from legitimate ones due to their high degrees of heterogeneity and similar characteristics. To solve this problem, a local feature engineering approach was proposed. This approach is based on the adoption of a data pre-processing strategy that allows reducing the number of network event patterns, increasing their characterization. The main distinguishing feature of the approach is that it operates locally in the feature space of each single network event, allowing to introduce new features and discretizing their values. The experimental results showed that the proposed approach improves the performance of known solutions. The results of the machine learning algorithm efficiency analysis for detecting cyberattacks in the Internet of Things infrastructure are presented in Table 1.  
The analysis of related works allows concluding that most studies had good detection accuracy; nevertheless, the main disadvantage of the investigated works is that they do not cover most features that may indicate the attack presence. The analysis shows that the known approaches for detecting IoT cyberattacks demonstrate high-efficiency levels.