Telekomunìkacìjnì ta ìnformacìjnì tehnologìï最新文献

{"title":"PROTECTION OF A HETEROGENEOUS TELECOMMUNICATION NETWORK FROM THE INFLUENCE OF DESTABILIZING FACTORS","authors":"A. P. Bondarchuk","doi":"10.31673/2412-4338.2023.010416","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.010416","url":null,"abstract":"Natural and anthropogenic hazards and their impact on a heterogeneous telecommunication network are analyzed. The currently existing telecommunication network protection measures have been analyzed. A heterogeneous network is considered, which consists of sections of communication lines with the transmission of signals of different physical nature over different transmission media. Communication lines react differently to threats, which allows you to choose the line with the best indicators for transmitting information. Examples of enhanced precautionary measures for the protection of underground line-cable structures are presented. A cause-and-effect diagram of events that determine the state of the information transmission network - changes in emergency/non-emergency time intervals - is presented. The scheme of application of protection measures against dangerous events is shown. To verify measures, a matrix of their compliance with typical natural disasters was developed and relevant examples were given. It is proposed to evaluate the flexibility of the telecommunications network by its connectivity, which is characterized by the numbers of vertex and edge connectivity, connectivity probability. An algorithm for calculating path connectivity has been built. The scheme of the device for carrying out multi-channel transmission of information in a hybrid network is presented, which allows the selection of the channel with the best indicators for the transmission of information. The general scheme of the operation of the intelligent block is proposed. A proposal has been put forward to increase the flexibility of the network, which consists in the use of this device. The article proposes to evaluate the flexibility of the telecommunications network by its connectivity. It is proposed to use machine learning in the management of a heterogeneous telecommunication network, which will allow predicting a destabilizing factor, its possible impact, and issue an algorithm for preventing the impact or solving the consequences of the impact.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135698937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NETWORK MONITORING TOOLS IN IOT INFRASTRUCTURE WITH HYBRID ARCHITECTURE","authors":"A. V. Kaplunov","doi":"10.31673/2412-4338.2022.022332","DOIUrl":"https://doi.org/10.31673/2412-4338.2022.022332","url":null,"abstract":"Text of annotation translation. – The processes of data exchange in computer networks have been examined within the context of generating productivity issues and critical network state in complex IoT systems with distributed infrastructure. Monitoring tools for computer network infrastructure in real-time were proposed for IoT systems. The architecture of a network environment monitoring system was developed to implement the concept of hybrid IoT infrastructure which was built with edge computing technologies. Also was proposed the methodology for integrating modern network infrastructure monitoring tools. The developed tools facilitate the collection, analysis and planning of performance components of IoT devices and their network environments. It enables the monitoring and prevention of critical states within the network IoT infrastructure in hybrid IoT system architectures. The proposed method for analysing computer network performance based on a weighted performance metric contributes to enhancing the efficiency of computer network monitoring within the local domain of network IoT infrastructure by load balancing. The developed tools was realized at the IoT edge, enhancing overall network monitoring efficiency at the local level and possessing the potential for integration with cloud technologies and services.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TWO-FACTOR USER AUTHENTICATION SYSTEM USING FACIAL RECOGNITION","authors":"A. P. Bondarchuk","doi":"10.31673/2412-4338.2023.039699","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.039699","url":null,"abstract":"In today's world of information technology, data security is becoming a paramount concern. One of the most effective protection methodologies is two-factor authentication. This article delves into a cutting-edge method of two-factor authentication based on the combination of neural networks and facial recognition. Deep learning, employed in neural networks, allows the system to adapt to minor changes in a user's appearance, such as a new hairstyle, the presence or absence of makeup, wearing glasses, and so on. This makes the system flexible and capable of recognizing the user even with slight alterations in their look. The core idea of the method is to analyze the unique features of the user's face. The neural network \"learns\" the characteristics of each user, creating their unique \"portrait\". This \"portrait\" is then used for identity verification upon attempting to access the system. In addition to facial recognition, the system may require password input or another form of authentication, making the login process even more secure. The combination of these two methods ensures a high level of protection against unauthorized access. A significant advantage of such a system is its convenience for the user. The user's face becomes the \"key\" to the system, making the login process quick and seamless. It's also worth noting that the advancement of facial recognition technology opens new horizons for data security. Using neural networks in conjunction with two-factor authentication may become the standard in the near future.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APPLICATION OF THE KUBEFLOW TOOL FOR THE INTEGRATION OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN UNMANNED AERIAL VEHICLE","authors":"M. Yu. Kuzmich","doi":"10.31673/2412-4338.2023.036679","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.036679","url":null,"abstract":"At the current stage of information technology development, machine learning (ML) and artificial intelligence (AI) are becoming one of the main tools for solving complex applied problems in various fields of activity. Various processes and technologies are used for develop, test, and maintain the infrastructure of the data system. The application of tools for the integration of ML and AI in the management of unmanned aerial vehicles (UAVs) is especially relevant today. An overview of the ML concept and processes (Machine Learning and Operation, MLOps) was made, which is a set of techniques for implementation and automatic continuous integration, as well as delivery to the product environment and model learning. The concept of MLOps is considered in terms of Kubeflow tools, which work on the Kubernetes platform. The possibilities of using modern MLOps solutions to improve the development processes of ML information systems have investigated. An AI-based information system with the possibility of continuous learning has designed. The concept of using the MLOps pipeline to solve the applied problem of classifying objects from the video of reconnaissance UAVs was presented. The results of the operation of the model in the Kubeflow arsenal have been checked using such improvement factors as: speed of development, implementation of changes, reduction of time to search for problems, recovery after global interruptions, reduction of the number of errors in the model. A publicly available model was deployed in a Kubeflow cluster using the Seldon Core Serving application manifest for practical analysis. The conducted research showed that Kubeflow consists of a set of various open source components that have a high level of integration with each other through the Kubernetes platform. At the same time, Kubeflow uses the Kubernetes pattern of operators for ML objects extremely efficiently. It has shown that writing model code is a small part of ML tasks, which affects the need for automation. The concept of a full-fledged information solution based on the continuous integration pipeline, which is the foundation of the implementation of the concept of continuous learning, has formed. Representing abstractions in the form of separate platform resources allows you to reduce the entry threshold for the end user.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135561788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APPLICATION OF DATA SCIENCE METHODS FOR DEMAND FORECASTING IN RETAIL","authors":"S.Yu. Haluzov","doi":"10.31673/2412-4338.2023.035965","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.035965","url":null,"abstract":"This scientific article examines the problem of forecasting demand in retail using data science methods. It is explained that traditional methods of demand forecasting do not give an excellent result, as machine learning, statistical models and data analysis become powerful tools, they need improvement, therefore this research is necessary and appropriate. The importance of accurate demand forecasting for effective inventory management, cost reduction, and customer service improvement is analyzed. The main methods of data science are considered, such as: machine learning, statistical models and data analysis. Real examples of the use of these methods in retail companies and their impact on increasing the accuracy of demand forecasting are also presented and clearly characterized for each company. Key steps in the forecasting process are described, including data collection and preparation, model selection, training, and performance evaluation. Examples of the use of machine learning algorithms, such as linear regression, decision trees, and neural networks, for demand forecasting in the retail sector are provided, and a comparison of these approaches is highlighted. The proposed price optimization procedure. This article shows that forecasting and analytics are integral to the effectiveness and competitiveness and flexibility of retailers in the market, and that the results of this study can be widely applied to further study the application of these methods, as well as to identify new methods. According to the scientific article, a conclusion was made that this research should be continued, and it will contribute to the effective functioning of retail companies and improve their competitiveness on the market. Recent achievements and prospects of using data science in demand forecasting are discussed.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WAVE FUNCTION COLLAPSE. APPLICATION OF COLLAPSE METHOD IN GAME SPACE GENERATION","authors":"V. V. Dziuba","doi":"10.31673/2412-4338.2023.034150","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.034150","url":null,"abstract":"The article explores a method for creating game content using procedural generation based on the wave function collapse. It has been determined that using this method with the basic approach yields relatively acceptable results. In some cases, anomalies are encountered, or spaces with a low level of diversity are generated. At the initial stages of generation, zones where generation is impossible using the basic method have been identified. One way to address this issue is through repeated generation, which requires additional resources. A modification of the method has been proposed using repeated passes. In some cases, this method has proven to be quite effective. To improve the method's efficiency, the space has been divided into zones to verify the correctness of the generated area. The most effective application of the basic method has been determined – traversing the space with another algorithm and post-processing after generation. One such algorithm allows for the preparation of samples to be substituted into closed zones that appear. Preparing generation materials significantly speeds up the operation of the wave function collapse. Preliminary traversal of space to identify probable problem areas occurs separately from the main generation. The algorithm has been modified for the automatic recognition of compatible samples, significantly reducing the time for generation preparation and the number of problem areas created. Enclosed spaces that restrict access to parts of the map have been identified. Depending on the size of the restricted area, several solutions have been proposed. For small areas, replacement with decorative content, for large areas – regeneration with samples designed to create internal content, or replacement of certain blocks with passable ones. Based on the research results, it is concluded that the generation of game space using the wave function collapse method is more efficient when auxiliary algorithms and constraints are applied at the initial and final stages of generation. Also, the idea of dividing generation into visible and invisible parts is proposed: the visible part is created during scene preparation, and the invisible part is generated during level traversal.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CREATION OF THE RESIT INFORMATION SYSTEM FOR PREDICTION OF RIVER POLLUTION UNDER EMERGENCY SITUATIONS","authors":"M. M. Gertsiuk","doi":"10.31673/2412-4338.2022.021322","DOIUrl":"https://doi.org/10.31673/2412-4338.2022.021322","url":null,"abstract":"This article describes developed RESit information system, that created, as improvement to RESit software. System parts are described, such as database, server, interaction between server and possible water flow measurement sensors, learning utility, administration application and user application. RESit advantages are given in relation to using similar systems possibility in emergencies. A practical implementation of such developed methods is described: • pollution forecasting results adjusting method based on a neural network working based on the regression problem; • pollution level forecasting method between specific points using interpolation and recursion methods; • pollution source determining method based on a filtering and sorting algorithm that works on the basis of facts database; • system information with sensors measuring current water flow interaction mechanism. Thus, an information system acquires information technology for forecasting river pollution in emergencies features.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DETECTION OF NETWORK ANOMALIES WITH NEURAL NETWORKS ALGORITHMS","authors":"H. I. Haidur","doi":"10.31673/2412-4338.2023.016173","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.016173","url":null,"abstract":"The rapid digitalization of the world has led to various attacks on computer systems and networks, so network security is an extremely important and relevant component of information security today. Creating effective cybersecurity tools and mechanisms is becoming increasingly difficult as the number of different devices and services grows. Identification of malicious traffic using deep learning methods has become a key component of intrusion detection systems (IDS). This article compares two deep learning models (recurrent neural network and convolutional neural network) for detecting anomalies in networks. Both neural networks were found to be useful in a wide range of applications. It has been shown that convolutional neural networks are best at detecting network anomalies in synergy with layers of long short-term memory. The development of deep learning technologies, including the considered neural network algorithms, is a promising direction in promoting the development of cybersecurity of information systems. These technologies are unique because they are at the initial stage of creation. The aforementioned technologies are currently not widespread in intrusion detection and network anomaly detection systems due to their novelty, so they require more thorough research. Conventional machine learning algorithms will eventually become insufficient, as they do not have such a good learning capability as deep learning neural networks do. The article provides a detailed analysis of the capabilities of recurrent and convolutional neural networks along with long short-term memory layers, which may be useful for use in further research.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYSIS OF NETWORK TRAFFIC THREATS ACROSS OSI MODEL LAYERS FOR DYNAMIC RTO CALCULATION IN THE CONTEXT OF COMBATING DDoS ATTACKS","authors":"H. I. Haidur","doi":"10.31673/2412-4338.2023.031221","DOIUrl":"https://doi.org/10.31673/2412-4338.2023.031221","url":null,"abstract":"This document provides an examination of current threats to network security, viewed through the lens of network traffic analysis at various OSI model layers. It delves into the different forms of Distributed Denial of Service (DDoS) attacks and their ramifications on the Transmission Control Protocol (TCP), with a specific focus on a critical parameter - the Retransmission Timeout (RTO). The text also divulges fundamental algorithms and techniques for calculating RTO, encompassing adaptive methodologies that harness machine learning and artificial intelligence for optimizing the TCP/IP stack. In particular, it offers insights into the functioning of the RTO calculation algorithm, a pivotal element ensuring the reliability of data transmission via TCP. The document elaborates on how this algorithm dynamically adjusts the RTO value based on network conditions and measured Round Trip Time (RTT) values. Furthermore, it furnishes formulas for computing RTO with diverse parameters. Moreover, the document explores the potential of employing machine learning and data analysis methodologies to detect and preempt DDoS attacks. It elucidates how contemporary technologies empower the use of these approaches to minimize false positives in identifying malicious traffic packets, thereby enhancing the effectiveness of safeguarding information systems. Additionally, it provides an illustration of software and hardware tools employed for the practical implementation of these algorithms in devices facilitating data transmission via Ethernet connections. In summary, this work offers insights into contemporary challenges and issues in the realm of network security, especially in the context of the escalating frequency of DDoS attacks. This information proves valuable for students and professionals engaged in the study of network security and the development of measures to fortify networks and systems.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135562295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MONITORING OF THE EDUCATIONAL PROCESS USING ARTIFICIAL INTELLIGENCE METHODS","authors":"O. V. Zinchenko","doi":"10.31673/2412-4338.2022.025362","DOIUrl":"https://doi.org/10.31673/2412-4338.2022.025362","url":null,"abstract":"Considered the problem of increasing the effectiveness of the educational process due to the introduction of automatic control of attendance in the classroom using face recognition and additional information for the collection and further analysis of the received data. Algorithms and methods used in modern Facial Recognition Attendance System are studied. An intelligent system for monitoring the educational process and its analysis is proposed. Structural and functional schemes of the system, databases, software were developed, testing was carried out. During attendance monitoring, the webcam captures an image of the face of the participant of the educational process from the video stream, then the computer automatically creates a vector of facial features, which is compared with the vectors of facial features, pre-entered images and recorded in the relevant database. Vectors with 68 features are used for face recognition. In the development of the software, the tools of the OpenCV library and the Python programming language were used. With several successful comparisons, the person's data is identified: name and status (student or teacher), the current date and time are recorded in an Excel file. Operational data of the system is displayed on the monitor screen, which allows you to correct recognition errors if necessary. The system allows you to automatically keep a log of class attendance, create reports, analyze data to provide recommendations for improving the schedule and order of lessons. The object-relational database with open source code PostgreSQL is used for data storage. The grouping of the system's software code is carried out using the Django web application. The system user has the opportunity to create a personal account and create reports according to his requirements. The system was tested on the example of a group of 15 students and showed satisfactory results.","PeriodicalId":494506,"journal":{"name":"Telekomunìkacìjnì ta ìnformacìjnì tehnologìï","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135610808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}