The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

An analysis of the physical characteristics of the node, which can be attacked by an attacker. A method of detecting a damaged node with a violation of the physical characteristics of the network node, which is based on the use of probability functions, calculation of the confidence interval and the probability of deviation of current values from the confidence interval. Its novelty lies in the possibility of detecting a damaged node by estimating the current value of the function in the confidence interval, without comparing the distribution function of the current node with the reference distribution. The analysis of physical parameters of network nodes for detection of the malefactor in contrast to existing systems of detection of attacks which allow to carry out only the analysis of network traffic is carried out. Based on the developed algorithm by modeling the transmission of chaotic signals in a wireless sensor network, the effectiveness of attack detection is determined through the analysis of residual energy and node congestion parameters, expanding the range of attacks that the network is able to counteract compared to system analogues. During the simulation of the behavior of the wireless sensor network, it was determined that the data transmission processes are chaotic. Therefore, to enhance the security of data transmission in a chaotic mode, we have proposed an encryption algorithm using dynamic chaos, coordinate delay methods and singular spectral analysis. A comparative analysis of the parameters of the input and output sequences of the developed encryption algorithm based on dynamic chaos with standard data encryption algorithms is performed. It is established that the encryption parameters that are characteristic of the original sequences of the encryption algorithm using dynamic chaos are not worse than the encryption parameters obtained for the source sequences of standard encryption algorithms. Estimation of node load by means of threshold analysis of their current values in the confidence interval is used to detect network deviations during a cyberattack. The developed algorithm allows to diagnose attacks such as "Denial of Service" and "Sibyl" at the beginning of their appearance and to determine possible ways to avoid them.

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