The article discusses the possibility of protecting the shore by disposing of dredged material at shallow depths. An example of a permanently eroded open marine shore segment located south of the Vistula Lagoon inlet (south-eastern part of the Baltic Sea) is considered. This shore segment is permanently caused by downstream erosion due to the moles bordering the entrance to the Vistula Lagoon (Baltiysk Strait) and interrupting longshore sediment transport. Changes of sediment distribution resulting from a demonstration disposal of clean fine sand at depths of seven to nine metres opposite the eroded segment of the shore are examined. A supplementary numerical modelling analysis of sediment transport for different winds showed that the disposed material is transported northward or southward alongshore depending on the wind direction, and almost none of it is stored at the shore slope. The demonstration disposal and numerical modelling results demonstrate that the only way to use the dredged material to protect the eroded shore near the inlet of the Vistula Lagoon is to dispose it directly onto the beach and not into the shallow water nearby.

<span>The paper considers the problem of protecting the Internet of things infrastructure against denial-of-service (DoS) attacks at the application level. The authors considered parameters that affect the network gateway workload: message frequency, payload size, number of recipients and some others. We proposed a modular structure of the attack detection tool presented by three classifiers that use the following attributes: username, device ID, and IP-address. The following types of classifiers have been the objects for the research: multilayer perceptron, random forest algorithm, and modifications of the support vector machine. Some scenarios for the behavior of network devices have been simulated. It was proved that for the proposed feature vector on simulated training and test data sets, the best results have been shown by a multilayer perceptron and a support vector machine with a radial basis function of the kernel and optimization with SMO algorithm. The authors also determined the conditions under which the selected classifiers have the best quality of recognizing abnormal and legitimate traffic in MQTT networks. </span>

The authentication algorithm for machine-to-machine communication devices via the MQTT Protocol is considered, which allows verifying the device legality without sending the password. The algorithm protects the Internet of things network from unauthorized access, ensures confidentiality by generating a common session key, and provides protection against attacks of the "man in the middle" and others. The obtained experiment results showed noticeable performance improvement compared to the TLS Protocol. The cryptographic strength of the proposed algorithm is based on the discrete logarithm problem. The main advantage of the algorithm is the ability to authenticate in one requestresponse cycle.

INFORMATION TECHNOLOGIES Contents 1. IntroduCtIon (287) 2. Methods and MaterIals (289) 3. researCh results (292) 4. dIsCussIon (283) 5. ConClusIon (293) referenCes (294) INTRODUCTIONRecently, there has been a significant popularity increase of technologies employed in the Internet. One of those technologies is the Internet of Things [1]. The main characteristic of the technology that allows uniting a variety of projects under a single name -the Internet of Things -is a possibility for a large number of devices, functioning without an operator, to communicate to carry out a single common task. The devices mentioned below must have only the essential capabilities. This makes them significantly cheaper than common workstation computers (personal computers, smartphones, etc.). Certain devices in the Internet of Things network function on an independent power supply. This imposes limits on the employment of such devices from the energy saving point of

Introduction: For the development of cyberphysical systems, new technologies and data transfer protocols are being developed, in order to reduce the energy costs of communication devices. One of the modern approaches to data transmission in cyberphysical systems is the publish-subscribe model, which is subject to a denial-of-service attack. Purpose: Development of a model for detecting a DoS attack implemented at the application level of publish-subscribe networks based on the analysis of their traffic using machine learning methods. Results: A model is developed for detecting a DoS attack, operating with three classifiers depending on the message type: connection, subscription, and publication. This approach makes it possible to identify the source of an attack. That can be a network node, a particular device, or a user account. A multi-layer perceptron, the random forest algorithm, and a support vector machine of various configurations were considered as classifiers. Training and test data sets were generated for the proposed feature vector. The classification quality was evaluated by calculating the F1 score, the Matthews correlation coefficient, and accuracy. The multilayer perceptron model and the support vector machine with a polynomial kernel and SMO optimization method showed the best values of all metrics. However, in the case of the support vector machine, a slight decrease in the prediction quality was detected when the width of the traffic analysis window was close to the longest period of sending legitimate messages from the training data set. Practical relevance: The results of the research can be used in the development of intrusion detection features for cyberphysical systems using the publish-subscribe model, or other systems based on the same approach