The article discusses modern machine learning algorithms used to detect and prevent denial of service (DoS) attacks. The work analyzes various approaches, such as traffic classification, system behavior anomaly, and network packet analysis, which allow the authors to develop an early warning system for possible attacks. The prospects for using machine learning to provide more reliable protection for network infrastructure are also discussed based on the results of the experiments. The results of the paper are of great scientific and practical value for specialists in cybersecurity and in modeling defense systems.

Keywords: information security, denial of service, machine learning, network traffic

Modern trends of the concretes studies have been considered. Properties of the materials and additives used have been cited. There has been undertaken designing an experiment on a complex modifying additive application for a fine fibre-reinforced fine-sand cocrete. The results of the studies undertaken have been analyzed with regard to their statistical significance. On the bases of the reseach results there have been acquired regression equations for objective functions responses, namely ultimate strength on both compression and bending, and average density of fine fibre-reinforced concrete. There have been produced contour plots and those of response function surface - ultimate strength under compression for all combinations of the both factors. There have been determined the composition of the modifying additive, which ensures maximal strength properties for fine fibre-reinforced fine-sand concrete.

Keywords: fine concrete, complex additive, designing an experiment, strength, regression equation, plots of response function surface

metamaterials, microwave, antennas, diffraction, Maxwell equations, electromagnetic waves, the dielectric constant

Keywords: metamaterials, microwave, antennas, diffraction, Maxwell equations, electromagnetic waves, the dielectric constant