## 2 Related Work
In addition, the adaptive Bayes network is used as a classification algorithm to predict and identify outliers in each node. [PERSON] et al. [15] consider a problem of secure decentralized data transmission, as well as a protection against attacks aimed at hacking keys for data encryption and authentication. A method is proposed for transmitting information from the nodes, and this method is resistant to such attacks. The Skipjack symmetric encryption algorithm is used. The main advantage of the proposed solutions is the use of the secret sharing scheme, which is based on solving the Lagrange polynomial. [PERSON] et al. describe issues of blocking flood attacks as they are highly relevant to wireless sensor networks [16]. The presented algorithm for blocking such attacks is based on the application of machine learning methods, comprising data preprocessing, model training, testing and evaluation. The solutions proposed by the authors are tested on CICIDS 2017 and CICDDDoS 2019 datasets, conducting experiments involving a range of ML methods, such as decision tree, Logistic Regression, etc. In addition, [PERSON] et al. discuss issues of defense against such types of attacks as phishing that could be observed in a WSN on an application layer of the interaction [17]. The authors propose an approach to detect phishing links based on machine learning. Such attributes as link URL composition, IP address, domain registration length and others are used for the detection. The Random Forest method is chosen as an appropriate ML method. Also, they implement a software module that allows for classifying phishing links by the character of the URL. The authors managed to achieve a fairly high accuracy of the detection. [PERSON] et al. expose a method for detecting malicious nodes in wireless sensor networks based on a correlation analysis [18]. The detection of a false data injection attack in WSNs is considered. The main concept of the method is to analyze the retrospective data (i.e., sensor readings) of each node in the network and evaluate their correlation with the current readings. The ARIMA model is used as a prediction mechanism in that work. Moreover based on the retrospective data, a prediction model for new data is built. If the actual data of a node do not match the prediction, it is concluded that there is an attack. The Ada-Boost method is used to resolve contradictions when a large percentage of confidence is assigned to a sentence that has a low possibility. To reduce the influence of environmental interference on the detection process, the authors developed a method for calculating the correlation coefficients and weights. Experimental testing of the proposed solutions is conducted on a WSN containing 600 sensor nodes designed to collect information about fires (temperature, humidity and smoke concentration). The experimental results show that the proposed method gives a better performance than other ones (the false-positive rate is below 3%, while the false-negative rate is below 30%). [PERSON] et al. discuss the detection of network attacks in software-defined networks (SDNs) using a self-organizing intelligent tunable attack detection environment with deep learning [19]. Possible anomalies that occur in SDNs and their defense methods are described. The authors rely on intrusion detection systems, machine learning and deep learning methods. More specifically, [PERSON] et al. propose a model of an intrusion detection system for SDN based on machine learning. The model implies a customizable concept of machine learning, which is designed to reduce the learning errors in the network and improve the detection accuracy. The authors use MATLAB/Python as software tools for implementing the model. In addition, a combined detection model that includes both machine learning and deep learning (DL) is also presented. Thus, ML and DL work in parallel, and their results are combined to make decisions on the intrusions. [PERSON] et al. present a hierarchical method for detecting blackhole attacks [20].