Vitalii Martovytskyi

@nure.ua

Department of Electronic Computers
Associate Professor, Deputy Head, dec.nure.ua



           

https://researchid.co/martovytskyi
21

Scopus Publications

Scopus Publications

  • DEVISING AN APPROACH TO PERSONALITY IDENTIFICATION BASED ON HANDWRITTEN TEXT USING A VISION TRANSFORMER
    Mykyta Shupyliuk, Vitalii Martovytskyi, Nataliia Bolohova, Yuri Romanenkov, Serhii Osiievskyi, Serhii Liashenko, Oleksii Nesmiian, Ihor Nikiforov, Vladyslav Sukhoteplyi, and Yevhen Lapchenkov
    Private Company Technology Center
    The object of this study is the approach to personality identification based on handwritten text using machine learning methods. Increasing the accuracy of personality identification and automating feature extraction could make it possible to perform more accurate analysis of handwritten text. A functional model has been built, and an experimental study of the proposed approach was conducted. The results of the study showed that the proposed approach increased the overall accuracy of identification, compared to other studies, as evidenced by the obtained accuracy values with the lowest indicator of 94.84 % for Friendliness and the highest 99.48 % for Conscientiousness. The accuracy indicator also improved compared to other models, as evidenced by the average accuracy value, which increased from 0.65 to 0.94. Such results were obtained through the use of the "Vision Transformer" method, which makes it possible to remove the need for feature extraction as a separate step, and the scale-invariant feature transformation approach made it possible to extract relevant image patches. An experimental validation was conducted using retrieval and classification approaches, which minimizes the variability of the results. The use of the Big Five model and the CVL dataset improves the accessibility of the study for comparison and reproducibility. In practice, handwriting analysis is widely used in forensics, for personnel selection, as well as in other areas of activity. The results increase the reliability of automated handwriting analysis systems in the area of personality identification, which could help graphologists and handwriting experts in their work both to assess personality traits and to determine whether a certain handwritten text belongs to a specific person

  • Method for Detecting FDI Attacks on Intelligent Power Networks
    Vitalii Martovytskyi, Igor Ruban, Andriy Kovalenko, and Oleksandr Sievierinov
    Springer Nature Switzerland

  • DEVELOPING A RISK MANAGEMENT APPROACH BASED ON REINFORCEMENT TRAINING IN THE FORMATION OF AN INVESTMENT PORTFOLIO
    Vitalii Martovytskyi, Volodymyr Argunov, Igor Ruban, and Yuri Romanenkov
    Private Company Technology Center
    Investments play a significant role in the functioning and development of the economy. Risk management is an integral part of the formation of the investment portfolio. This means that an investor must be willing to take on a certain level of risk in order to receive a certain level of return. However, when forming an investment portfolio, an investor faces such problems as market unpredictability, asset correlation, incorrect asset allocation. Therefore, when forming an investment portfolio, an investor should carefully study all possible risks and try to minimize them. The object of research is an approach to risk management in the formation of an investment portfolio using the method of reinforcement training. The basic principles of formation of the investment portfolio and determination of risks are described. The application of the method of reinforcement training for building a model of risk management of investment portfolio is considered. The process of selecting optimal investment assets based on alternative data sources that minimize risks and maximize profits is also considered. A functional model of the process of risk optimization in the formation of an investment portfolio based on machine learning methods has been developed. The functional model constructed makes it possible to build a process of risk optimization, including asset selection, risk comparison and assessment, to form an investment portfolio and monitor its risks. The study results showed that the proposed approach to the formation of the investment portfolio increased the total growth of the investment portfolio by 0.4363 compared to the base model. Also, the volatility indicator improved compared to the market, as evidenced by the percentage difference between the initial and final cash amount, which increased from 128.98 to 295.57

  • DEVISING AN APPROACH TO THE IDENTIFICATION OF SYSTEMUSERS BY THEIR BEHAVIOR USING MACHINE LEARNING METHODS
    Vitalii Martovytskyi, Оleksandr Sievierinov, Oleksii Liashenko, Yuri Koltun, Serhii Liashenko, Viktor Kis, Vladyslav Sukhoteplyi, Andrii Nosyk, Dmytro Konov, and Dmytro Yevstrat
    Private Company Technology Center
    One of the biggest reasons that lead to violations of the security of companies’ services is obtaining access by the intruder to the legitimate accounts of users in the system. It is almost impossible to fight this since the intruder is authorized as a legitimate user, which makes intrusion detection systems ineffective. Thus, the task to devise methods and means of protection (intrusion detection) that would make it possible to identify system users by their behavior becomes relevant. This will in no way protect against the theft of the data of the accounts of users of the system but will make it possible to counteract the intruders in cases where they use this account for further hacking of the system. The object of this study is the process of protecting system users in the case of theft of their authentication data. The subject is the process of identifying users of the system by their behavior in the system. This paper reports a functional model of the process of ensuring the identification of users by their behavior in the system, which makes it possible to build additional means of protecting system users in the case of theft of their authentication data. The identification model takes into consideration the statistical parameters of user behavior that were obtained during the session. In contrast to the existing approaches, the proposed model makes it possible to provide a comprehensive approach to the analysis of the behavior of users both during their work (in a real-time mode) and after the session is over (in a delayed mode). An experimental study on the proposed approach of identifying users by their behavior in the system showed that the built patterns of user behavior using machine learning methods demonstrated an assessment of the quality of identification exceeding 0.95

  • Self-healing Systems Monitoring
    Igor Ruban, Vitalii Martovytskyy, and Olesia Barkovska
    Springer International Publishing

  • Mathematical Model of User Behavior in Computer Systems
    Vitalii Martovytskyi, Igor Ruban, Oleksandr Sievierinov, Andrii Nosyk, and Valentyn Lebediev
    IEEE
    Various electronic keys (tokens, cards, etc.) are also quite common in the quality of identifiers. But it should be noted that in recent years, more and more widespread identification systems that use biometric characteristics of man in solving the problem of access to information systems. Therefore, the task of developing models of behavior of users of computer systems, which take into account both dynamic and static properties of the behavior of users, as well as their possible ones, is urgent. The paper presents a comprehensive approach to the analysis of user behavior in order to identify anomalies in its work. This approach should take into account the dynamic and statistical properties of the behavior, as well as possible changes in behavior that are not associated with anomalies.

  • Method of Detecting FDI Attacks on Smart Grid
    Vitalii Martovytskyi, Igor Ruban, Hennadiy Lahutin, Irina Ilina, Volodymyr Rykun, and Vladyslav Diachenko
    IEEE
    Nowadays energy systems in many countries improve and develop being based on the concept of deep integration of energy as well as infocomm grids. Thus, energy grids find the possibility to analyze the state of the whole system in real time, to predict the processes in it, to have interactive cooperation with the clients and to run the appliance. Such concept has been named Smart Grid. This work highlights the concept of Smart Grid, possible vectors of attacks and identification of attack of false data injection (FDI) into the flow of measuring received from the sensors. Identification is based on the use of spatial and temporal correlations in Smart Grids.

  • Functional Model of Computer Networks Security Information
    Dmytro Holubnychyi, Vitalii Martovytskyi, Igor Ruban, Oleksandr Sievierinov, Valentyn Lebediev, and Vyacheslav Tretiak
    IEEE
    Information security management allows the use of data in the system, while ensuring their security and the security of computing resources. Confidentiality, integrity and availability are three main components of information security that can always be invoked by a number of methods, but we will focus on the complexities of the computer system and network management. In addition, criteria for the safety of personnel, in particular their safety, will be developed.

  • Measuring Vulnerabilities in Threat Modelling with Risk Matrix
    Andrii Hapon, Volodymyr Fedorchenko, Vitalii Martovytskyi, Volodymyr Rykun, Oleksandr Sievierinov, and Inna Oleshko
    IEEE
    Threat modeling is one of the most important parts when it comes to security in development of programing product. The main challenges for that are time and prioritization of the scope of work. Risk matrix is effective tool for making clear what should be done first and which consequences can be. There are few levels of consequences which are ranged by the influence on business. With help of vul-nerability assessment threats can be measured by impact on confidentiality, in-tegrity, and availability. The Common Vulnerability Scoring System is appropri-ate tool for catching the principal characteristics of a vulnerability and produce numerical score reflecting its severity.

  • DEVELOPMENT OF METHODS FOR GENERATION OF DIGITAL WATERMARKS RESISTANT TO DISTORTION
    Vitalii Martovytskyi, Igor Ruban, Nataliia Bolohova, Оleksandr Sievierinov, Oleg Zhurylo, Oleksandr Permiakov, Andrii Nosyk, Dmytro Nepokrуtov, and Ivan Krylenko
    Private Company Technology Center
    Active attacks and natural impacts can lead to two types of image-container distortions: noise-like and geometric. There are also image processing operations, e.g. scaling, rotation, truncation, pixel permutation which are much more detrimental to digital watermarks (DWM). While ensuring resistance to removal and geometric attacks is a more or less resolved problem, the provision of resistance to local image changes and partial image deletion is still poorly understood. The methods discussed in this paper are aimed at ensuring resistance to attacks resulting in partial image loss or local changes in the image. This study's objective is to develop methods for generating a distortion-resistant digital watermark using the chaos theory. This will improve the resistance of methods of embedding the digital watermark to a particular class of attacks which in turn will allow developers of DWM embedding methods to focus on ensuring the method resistance to other types of attacks. An experimental study of proposed methods was conducted. Histograms of DWMs have shown that the proposed methods provide for the generation of DWM of a random obscure form. However, the method based on a combination of Arnold’s cat maps and Henon maps has noticeable peaks unlike the method based on shuffling the pixels and their bits only with Arnold’s cat maps. This suggests that the method based only on Arnold’s cat maps is more chaotic. This is also evidenced by the value of the coefficient of correlation between adjacent pixels close to zero (0.0109) for color DWMs and 0.030 for black and white images.

  • Method for Determining the Structural Reliability of a Network Based on a Hyperconverged Architecture
    Igor Ruban, Heorhii Kuchuk, Andriy Kovalenko, Nataliia Lukova-Chuiko, and Vitalii Martovytsky
    Springer International Publishing

  • Development of a Method for Improving Stability Method of Applying Digital Watermarks to Digital Images
    Oleksandr Makoveichuk, Igor Ruban, Nataliia Bolohova, Andriy Kovalenko, Vitalii Martovytskyi, and Tetiana Filimonchuk
    Private Company Technology Center
    A technique for increasing the stability of methods for applying digital watermark into digital images is presented. A technique for increasing the stability of methods for applying digital watermarks into digital images, based on pseudo-holographic coding and additional filtering of a digital watermark, has been developed. The technique described in this work using pseudo-holographic coding of digital watermarks is effective for all types of attacks that were considered, except for image rotation. The paper presents a statistical indicator for assessing the stability of methods for applying digital watermarks. The indicator makes it possible to comprehensively assess the resistance of the method to a certain number of attacks. An experimental study was carried out according to the proposed method. This technique is most effective when part of the image is lost. When pre-filtering a digital watermark, the most effective is the third filtering method, which is averaging over a cell with subsequent binarization. The least efficient is the first method, which is binarization and finding the statistical mode over the cell. For an affine type attack, which is an image rotation, this technique is effective only when the rotation is compensated. To estimate the rotation angle, an affine transformation matrix is found, which is obtained from a consistent set of corresponding ORB-descriptors. Using this method allows to accurately extract a digital watermark for the entire range of angles. A comprehensive assessment of the methodology for increasing the stability of the method of applying a digital watermark based on Wavelet transforms has shown that this method is 20 % better at counteracting various types of attacks

  • Bio-Inspired Optimization of Rateless Codes for Reliable Intelligent Transportation Systems
    Sergii Prykhodko, Mykola Shtompel, Oleksandr Sievierinov, Viacheslav Tretiak, Andrii Vlasov, and Vitalii Martovytskyi
    Springer International Publishing

  • Comparative efficiency analysis of gradational correction models of highly lighted image
    Kirill Smelyakov, Mykyta Hvozdiev, Anastasiya Chupryna, Denys Sandrkin, and Vitalii Martovytskyi
    IEEE
    The paper provides a comparative analysis of the application of the most demanded gradational correction models (power, exponential and logarithmic) of a highly lighted digital image, which capable of automatic adaptation to different brightness scales, discusses the features of their practical application, sets up an experiment to improve the highly lighted photo. In the experiment, the test image is modified with use of different gradation correction models and different parameters, this helps to show the practical value of such modifications and the coefficient of image enhancement is given to provide comparative analysis of influence of input parameters to final result. After considering the results of the experiment, analysis and recommendations for the practical use of the models are given which helps to solve actual applied tasks of digital image quality improvement

  • Efficiency of image convolution
    Kirill Smelyakov, Mykyta Shupyliuk, Vitalii Martovytskyi, Dariia Tovchyrechko, and Oleksandr Ponomarenko
    IEEE
    The article discusses the main algorithms used to convolve a digital image, experiment is performed on various reduction factors, and discusses the use of convolution algorithms for an image with a large number of fine details, analyzes the effectiveness of the experimental results and selects the most effective convolution algorithms used for images with a large number small parts.

  • Identification in Informative Systems on the Basis of Users' Behaviour
    I.V. Ruban, V. O. Martovytskyi, A. A. Kovalenko, and N. V. Lukova-Chuiko
    IEEE
    Distributed informative systems which unite the technology of client’s server with global web have posed numerous problems. It turned out that standard methods of identification have already become obsolete. Particularly the problem is that the generally accepted division of methods of a control over physical access and a control over access to information are not effective any more. To solve this problem there is a need to apply for the methods of identification which will have possibility to identify users with the help of aggregate of actions implemented by users in the process of work with DIS.

  • Method of neural network recognition of ground-based air objects
    Igor Ruban, Kirill Smelyakov, Martovytskyi Vitalii, Pribylnov Dmitry, and Nataliia Bolohova
    IEEE
    Present article describes the method, which allows to define a type of air vehicle in automatic mode during the processing of optoelectronic scouting images. The method is based on neural network technology of images recognition with processing of a prior received data by the network itself. This makes possible to increase the probability of correct recognition, as well as to take into account potential errors of exact parameters.

  • Search by Image. New Search Engine Service Model
    Kirill Smelyakov, Denys Sandrkin, Igor Ruban, Martovytskyi Vitalii, and Yury Romanenkov
    IEEE
    Every year millions of photos and images appear in the Internet. Most of them are downloaded to personal cloud storage or become available to the public. With such a huge amount of information, the need for an effective search in the picture ripens. And if excellent tools have already been created for text search, image search remains an unresolved problem. The purpose of this publication is to develop a model for creating an effective image search service.

  • Investigation of network infrastructure control parameters for effective intellectual analysis
    Kirill Smelyakov, Pribylnov Dmitry, Martovytskyi Vitalii, and Chupryna Anastasiya
    IEEE
    One of the important problems faced by current intrusion detection systems, monitoring systems is the processing of large data sets that complicates the intellectual processing of data. To detect a variety of cases of threats and violations, the monitoring system should control a large number of parameters. Therefore, selection of characteristics is an important stage in the construction of algorithms for machine learning. This stage is necessary to get rid of noise attributes and due to this improve the quality and speed up the work of algorithms. The conducted experiments confirm that the algorithms for selecting attributes using Random Forest and the methods of filtration effectively manage their task.


  • Designing a monitoring model for cluster supercomputers
    Igor Ruban, Vitalii Martovytskyi, and Nataliia Lukova-Chuiko
    Private Company Technology Center
    Recently there has been an increase in the number of cyber attacks against computational systems. Growth in the amounts of information that passes through computational clusters and savings on staff requires an application of effective means of monitoring computational resources for the purpose of prediction and elimination of cyber attacks. An analysis of hacker attacks revealed that the break-in was not detected by technical equipment. We examined a concept of building existing systems of monitoring of cluster super-computers. Deficiencies are established in the monitoring systems, which lead not only to the reduction in efficiency of computational clusters but to their safety violations. We described a formal model for the detection of anomalies in the functioning of a computational cluster. The model is the sets of the states of the system depending on functional tasks, it separates processes of targeted functioning of the system from the interface processes of interaction with the network infrastructure and provides for the possibility of their use in neural network technology for detecting anomalies in the functioning of a computational cluster. This model makes it possible to locally control parameters for each process and, based on the formed vector, to detect anomalous influence on the system as a whole. Data of the study can be used for the improvement of already existing subsystems of monitoring of super-computer technologies, as well as form a foundation for creating fundamentally new neural network multi-agent system of monitoring of the detection of anomalous incidents in the performance of computational clusters.