TELECOMMUNICATION AND INFORMATION TECHNOLOGIES

Title

Mon, 06 Jan 2025 08:25:47 +0000

Scientific Journal

No. 4 (85) 2024

Content

Mon, 06 Jan 2025 08:28:44 +0000

Content

The ways of advancement of transport operations for the advance of dark logistic results

Mon, 06 Jan 2025 09:29:16 +0000

Globalization and technological progress have transformed logistics into a rich field, which is driven by the integration of different types of activities and the advancement of digital innovations, beyond dark technologies. This article is dedicated to the problem of implementing critical solutions in the transport and logistics sector. It reveals important elements of the development of dark technologies, their warehouse and characteristics. Descriptions of the service model, as well as: Desktop as a service (DaaS), Platform as a service (PaaS), Infrastructure as a service (IaaS) and models of the throat: private, public, public and hybrid. Based on the analysis of scientific literature and cases of successful companies, it has been proven that the rise of cloudy platforms in transport logistics is a key advantage over traditional methods of transport management. As a result of the development of optimal routes and monitoring of transport processes in real time, this technology significantly increases the efficiency of logistics processes, reducing waste on fuel, accommodating a change in cash flows speech, quick delivery time and the ability to follow up, which allows us to ensure a high level of customer service. The investigation also revealed evidence of the stagnation of bad technologies related to data security, integration of bad solutions with basic transport management systems, and the need for mobility qualifications of personnel and the inability of small and medium-sized businesses to adapt to the introduction of new technologies through the availability of necessary resources and competencies. The directions for improving transport operations using cloud technologies are described, including the integration of artificial intelligence, machine learning and IoT; improving technologies focused on sustainability and environmental friendliness; implementing blockchain to improve information security; and applying robotics and automation in warehouses. As an example, the process of developing a methodology for organizing van-passenger transportation based on advanced logistics technologies for small and medium-sized businesses is described: establishing capabilities for the operation of this add-on; the development program has been designated; the diagram of variants of the wiki was modeled; the security software for Windows 11 was disassembled and tested. The results of the study can be implemented in the educational process of students 12 Information technology.

Keywords: cloud technologies, transport logistics, optimization, logistics transparency, environmental friendliness, security, small and medium-sized businesses.

References

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Enhancing the Kuramoto model for modeling information dissemination in social networks

Дмитрієнко Катерина Анатоліївна (Dmytriienko Kateryna) — Mon, 06 Jan 2025 09:51:59 +0000

The article explores enhancements to the Kuramoto model for analyzing information dissemination in social networks by integrating additional parameters and modifications. Originally designed for studying synchronization in physical and biological systems, the Kuramoto model has proven its effectiveness in social sciences, particularly for modeling collective user behavior in social networks. The paper introduces several improved versions of the Kuramoto model. First, integrating principles from the cascade model enables the consideration of the probability of information transfer between users, significantly increasing the accuracy of representing real processes. This enhancement allows for forecasting peak moments of content dissemination and its viral potential, which is particularly beneficial for optimizing marketing campaigns. Second, adapting the Kuramoto model using the epidemic model facilitates modeling the dynamics of information spread, accounting for users transitioning between states: susceptible, infected, and recovered. This allows for the modeling of recurring waves of information popularity, analyzing its decline, and planning long-term information campaigns. The third improvement is based on the application of the rumor spreading model, which accounts for changes in user behavior influenced by social connections and trust levels. This modification provides more accurate modeling of information flows in social networks, aiding in forecasting viral content and combating misinformation. The fourth enhancement involves accounting for the impact of key network nodes through the integration of the influential user model. This approach enables modeling the effect of opinion leaders on system synchronization, improving the prediction of content dissemination and the efficiency of information campaigns in social networks. Comparative studies between the baseline and enhanced models demonstrate the significant advantage of the latter in achieving synchronization among network nodes, which is particularly critical for the rapid dissemination of information in large networks. Graphs presented in the article visually illustrate the effectiveness of these modifications. Future research proposes expanding the Kuramoto model by incorporating factors such as emotions, social status, and dynamic changes in connections. An important task is verifying the results on large datasets from social networks and optimizing computational algorithms for the model's application in large-scale networks. These developments open prospects for creating effective tools for forecasting viral content, managing information flows, and combating misinformation. Thus, the article offers a new perspective on modeling social processes, demonstrating the universality and effectiveness of the Kuramoto model for analyzing information dissemination in complex networks.

Keywords: Kuramoto model, synchronization, information dissemination, cascade model, epidemic model, rumor spreading model, influential user model.

References

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Dmytriienko, К. Adaptation of the kuramoto model for theanalysis of the distribution of information in social networks. Cybersecurity: Education, Science, Technology. Electronic Professional Scientific Journal, 2023, 1, 309-314. https://doi.org/10.28925/2663- 4023.2023.21.309314
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Chiba H., Medvedev G. S., Mizuhara M. S. Bifurcations in the Kuramoto model on graphs. Chaos: an interdisciplinary journal of nonlinear science. 2018. Vol. 28, no. 7. P. 073109. URL: https://doi.org/10.1063/1.5039609.
Yang, Y., Lu, Z., Li, V. O. K., & Xu, K. Noncooperative information diffusion in online social networks under the independent cascade model. IEEE Transactions on Computational Social Systems, 2017, 2017. Vol. 4, no. 3. P. 150–162. https://doi.org/10.1109/tcss.2017.2719056
Rodrigues, F. A., Peron, T. K. D., Ji, P., & Kurths, J. The Kuramoto model in complex networks. Physics Reports, 2017, Vol 610, P. 1–98. https://doi.org/10.1016/j.physrep.2015.10.008.
Phillips E. T. The synchronizing role of multiplexing noise: Exploring Kuramoto oscillators and breathing chimeras. Chaos: an interdisciplinary journal of nonlinear science. 2023. Vol. 33, no. 7. URL: https://doi.org/10.1063/5.0135528.
Ulichev O. S. Research on information dissemination models and information impacts in social networks. Control, Navigation, and Communication Systems: Collection of Scientific Works 2018. Т. 4, № 50. С. 147–151. URL: https://doi.org/10.26906/sunz.2018.4.147.
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Zhu L. Synchronization dynamics in the Sakaguchi-Kuramoto oscillator network with frequency mismatch rules. Journal of applied mathematics and physics. 2020. Vol. 08, no. 02. P. 259– 269. URL: https://doi.org/10.4236/jamp.2020.82021.

Methodology for constructing causal networks in cybersecurity using generative artificial intelligence

Mon, 06 Jan 2025 10:10:18 +0000

This article proposes a methodology for the formation of causal networks in the field of cybersecurity using generative artificial intelligence (GAI). The methodology is based on a hierarchical approach to AI systems, such as ChatGPT, to determine the central node and levels of the hierarchy, as well as to further clarify the causal relationships. The essence of the proposed methodology is to determine the central node and hierarchy levels, form a set of related concepts, visualize the primary casual network, interact with a swarm of virtual experts to improve the accuracy and completeness of the network, and form the final causal network. The possibility of using the Gephi program to visualize a graph representing a casual network is considered. The article presents a methodology for selecting and applying a significance threshold for filtering insignificant connections in order to form a more accurate and complete final causal network for further scenario analysis in the field of cybersecurity. Various options for applying the significance threshold are considered, depending on the characteristics of the network, prior knowledge or analysis of training data, as well as on the basis of statistical indicators such as the average weight and standard deviation. The possibility of dynamically adjusting the significance threshold based on an assessment of the quality of the final network, taking into account such indicators as the number of clusters, network cohesion, and the significance of links, is analyzed. Examples of queries to the GCI systems and the results of their execution are presented, which allow us to better understand the process of network formation. Experimental results show that the proposed methodology allows to effectively form causal networks that can be used for further scenario analysis in the field of cybersecurity.

Keywords: Cybersecurity, generative artificial intelligence, ChatGPT, hierarchical approach, virtual experts, scenario analysis, causal networks, Gephi, text analytics, network analysis.

References

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A probable model of establishing information interaction in the internet of things network with Mesh topology

Жидка Ольга Валеріївна (Zhydka Olga) — Mon, 06 Jan 2025 10:28:05 +0000

The article proposes an approach to evaluating the probabilistic and temporal characteristics of information interaction in Internet of Things networks. Designing Internet of Things systems involves solving tasks related to modeling information interaction processes. This enables the determination of optimal operating modes for such systems based on predicted traffic. The core concept of the Internet of Things lies in organizing the interaction between various objects in the environment, transmitting the information they generate, and ensuring a stable connection. Considering the novelty, fundamental characteristics, and complexity of Internet of Things systems, modeling and the development of relevant algorithms are key research tools during the design stage, which highlights the relevance of this work. The paper proposes a probabilistic model for establishing information interaction in an Internet of Things network with a mesh topology, developed using a multi-agent approach. The model accounts for the fundamental characteristics of Internet of Things and allows the evaluation of both absolute and probabilistic parameters of interaction. The model incorporates conditions corresponding to real-world information interaction processes, including faulty channels and access points, a limited number of reconnection attempts, and the presence of alternative routes.

To evaluate data transmission time, the Laplace-Stieltjes transform method is proposed. Its first central moment enables determining the average data transmission time within an established connection, while the probabilistic interpretation of the Laplace-Stieltjes transform allows assessing the probability of data delivery. The method provides an analysis of the time distribution for k-class data within an IoT network, based on given probabilities of errors occurring on route elements between all pairs of sensor devices. Using the Laplace-Stieltjes transform, it is possible to evaluate the permissible load on a route under a time constraint and, accordingly, select an optimal network self-organization algorithm.

Keywords: Internet of Things, IoT, probabilistic model, sensor device, sensor networks, information interaction.

References

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Shafique K., Khawaja B.A., Sabir F., Qazi S., Mustaqim M. Internet of Things (IoT) for nextgeneration smart systems: A review of current challenges, future trends and prospects for 5G-IoT scenarios // IEEE Access. – 2020. – Vol. 8. – P. 23022–23040.
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Information systems of mobile monitoring of marine waters and coastal zones

Волинець Тарас Васильович (Volynets Taras) — Mon, 06 Jan 2025 10:33:35 +0000

The work is dedicated to the solution of the scientific and practical task, which consists in the development of methods, technologies and means of creating systems of complex monitoring of marine water areas and the state of coastal zones using unmanned aerial vehicles (UAVs). The main goal is to increase the quality, efficiency, complexity and effectiveness of the processes of data collection of observations, their processing, transmission, preservation and analysis of information about the state of the territory and its environmental safety. The research focuses on the combination of the complexity of the methodology of building a dynamic system of monitoring, forecasting and warning regarding the security and sustainable development of the territories and the country as a whole. The main information base consists of satellite data and data obtained with the help of UAVs regarding emergency situations and the state of marine water areas. A special feature of the proposed methodology is the newly introduced structural elements that allow determining the composition of on-board equipment, the number of UAVs and the optimal route of their movement in accordance with the fulfillment of the environmental tasks. It has been proven that the methods of mathematical and simulation modeling contribute to the creation of functional and informational models, and the methods of system analysis are also used to establish structural connections between elements of complex systems. The cartographic method of researching the objects of sea water areas and coastal zones includes cartographic modeling and regional analysis of the spatial structure of geoecological phenomena, which allows determining their ecological criteria. On the basis of the application of the load-bearing equipment on board the UAV, proposals were implemented for qualitative assessment and control of environmental parameters when solving the tasks of ecological monitoring of the state of marine water areas and coastal zones.

Keywords: information technology, ecosystem, marine water areas, software trajectory, control system, control algorithms, spectral channels, remote methods.

References

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System for monitoring of parking spaces using computer vision

Mon, 06 Jan 2025 10:40:29 +0000

This article explores the potential of a prototype system that monitors the occupancy of parking spaces. Through in-depth research, the paper highlights the problems faced by drivers and the shortcomings of traditional methods of solving them. To effectively achieve this goal, we rely heavily on numerous computer vision technologies. Image processing algorithms are used by the system to actively detect free or occupied parking spaces, and this happens in real time. Efficient processing of the video stream is provided by OpenCV libraries, which are used for image transformation, adaptive thresholding, contour analysis, and parking lot status identification. The system shows free parking spaces and displays information on the screen. Thanks to the use of this technology, the process of monitoring parking spaces is automated, resulting in a reduction in the time required to find free spaces, which optimizes the overall use of parking areas. The system can be very useful for improving modern smart transportation systems and can also help organize smart parking spaces. The system has great potential and prospects for further research, as well as integration into modern intelligent transportation systems, in particular for organizing smart parking lots and improving parking space management.

Keywords: system, computer vision, parking lot monitoring, OpenCV, image processing, video analytics, free parking space detection, intelligent transportation systems.

References

Giampaoli L. E., Hessel F. (2021) Parking Space Occupancy Monitoring System Using Computer Vision and IoT, IEEE 7th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA. pp. 7-12, doi: 10.1109/WF-IoT51360.2021.9595935.
Kuzela M., Fryza T., Zeleny O. (2024) Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study, 13th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro. pp. 1-4, doi: 10.1109/MECO62516.2024.10577808.
Dixit M., Srimathi C., Doss R., Loke S., Saleemdurai M. A. (2020) Smart Parking with Computer Vision and IoT Technology, 43rd International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy. pp. 170-174, doi: 10.1109/TSP49548.2020.9163467.
Popereshnyak S., Yurchuk I. (2021) Car Parking Data Processing Technique for Smart Parking System as Part of Smart City. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham.
Zhang, Y.; Chen, P. Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm. Sensors 2023. № 23, P. 9802.
Ganenko L. D., Zhebka V. V. Analytical review of issues of navigation of mobile robots in closed spaces. Telecommunications and information technologies. 2023. No. 3(80). Art. 85-98.
Malinov V., Zhebka V., Zolotukhina O., Franchuk T., Chubaievskyi V. Biomining as an Effective Mechanism for Utilizing the Bioenergy Potential of Processing Enterprises in the Agricultural Sector. CEUR Workshop Proceedings. 2023, 3421, p. 223–230

Complex approach to business requirements management in electronic commerce systems

Mon, 06 Jan 2025 10:50:37 +0000

The article examines the main approaches to managing business requirements used in ecommerce projects, including classical and agile methodologies such as Waterfall, Agile, Scrum and Kanban. The advantages and disadvantages of each approach are analyzed, as well as their impact on key performance indicators of projects: implementation terms, quality of the final product, and costs. Given the specifics of ecommerce, special attention is paid to issues of adaptability to changes, integration with other systems, omnichannel support and data security. The article also includes an analysis of modern business requirements management tools, such as Jira, Trello, and Microsoft Azure DevOps, which are widely used to streamline workflows and automate requirements management. A comparative analysis of these tools was carried out, taking into account their capabilities for different types of projects, scaling, support for flexible approaches and integration with other software solutions. Based on this analysis, recommendations on the choice of tools are offered depending on the size of the company and the specifics of the project. Particular attention is paid to the role of the latest technologies, such as artificial intelligence and machine learning, in the process of managing business requirements. The possibilities of automating the processes of requirements collection and analysis, prioritization, testing and monitoring are considered. Practical recommendations for the implementation of such technologies are offered to improve the efficiency of requirements management and reduce risks associated with changes in the market. The article also provides recommendations for improving the business requirements management process aimed at optimizing resources, improving product quality, and reducing costs. Implementation of flexible management practices, regular review of requirements, automation of testing and use of analytics for decision-making are proposed. This will allow companies to quickly adapt to changing market conditions, increase the level of customer satisfaction and strengthen their competitiveness. The results of the study are useful for project managers, business analysts and developers who work in the field of e-commerce and seek to improve the processes of requirements management, ensuring the successful implementation of projects.

Keywords: business requirements management, e-commerce, methodologies, tools, project effectiveness, competitiveness.

References

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Ahmad, M. O., Dehghantanha A. (2019) Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. Springer.
Li P., Yanchinda J. The Customer Requirements About Thai SMEs Product Based on Customer Knowledge Management by Using Text Mining, 2024 5th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), Bangkok, Thailand, 2024, pp. 1-5, doi: 10.1109/TIMES-iCON61890.2024.10630737.
Xiao Z., Wang X., Sheng B., Miao Z., Shu Y. Customer requirement information mapping method for product module configuration, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi'an, China, 2016, pp. 921-924, doi: 10.1109/IMCEC.2016.7867345.
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Artificial intelligence and social networks: approaches to detecting fake information

Mon, 06 Jan 2025 10:58:19 +0000

The article examines modern approaches to detecting fake information on social networks using artificial intelligence (AI) and machine learning (ML). The growing popularity of social networks is accompanied by a global disinformation problem, which has severe implications for society, the economy, politics, and even public health, as was especially evident during the COVID-19 pandemic. Particular attention is given to methods of detecting fake news, including the use of neural networks, user psychological profiling, and text analysis. Additionally, the study explores approaches to analyzing visual content, such as images and videos, to assess their authenticity. The article also analyzes the role of social bots in disseminating disinformation, including their ability to influence public opinion through manipulation, as well as tools for identifying them, such as graph models and heuristic methods. The study characterizes data collection and processing techniques, including social platform APIs, web scraping, user activity monitoring, and interaction analysis. Special attention is devoted to data preprocessing steps, including cleaning, normalization, tokenization, lemmatization, and annotation, which significantly impact the quality of algorithm performance in detecting fake information. The challenges of scalability and system efficiency in the context of large data volumes are discussed, along with issues related to ensuring user privacy. Furthermore, the article highlights the necessity of adapting algorithms to new patterns of disinformation, which evolve in response to technological advancements, and the importance of an interdisciplinary approach that combines achievements in AI, cognitive science, linguistics, and sociology.

Keywords: artificial intelligence, social media, fake news, machine learning, disinformation, neural networks, news detection, data processing.

References

Silva, F., Vieira, R., & Garcia, A. Can machines learn to detect fake news? A survey focused on social media. Hawaii international conference on system sciences - HICSS. 2019. P. 1–8. URL: https://doi.org/10.24251/HICSS.2019.332. ISSN 2412-4338 Телекомунікаційні та інформаційні технології. 2024. № 4 (85)
Arming the public with artificial intelligence to counter social bots / K. Yang et al. Human behavior and emerging technologies. 2019. Vol. 1, no. 1. P. 48–61. URL: https://doi.org/10.1002/hbe2.115 (date of access: 03.11.2024).
The mass, fake news, and cognition security / B. Guo et al. Frontiers of computer science. 2020. Vol. 15, no. 3. URL: https://doi.org/10.1007/s11704-020-9256-0 .
Nistor A., Zadobrischi E. The influence of fake news on social media: analysis and verification of web content during the COVID-19 pandemic by advanced machine learning methods and natural language processing. Sustainability. 2022. Vol. 14, no. 17. URL: https://doi.org/10.3390/su141710466
Bio-Inspired artificial intelligence with natural language processing based on deceptive content detection in social networking / A. A.Albraikan et al. Biomimetics. 2023. Vol. 8, no. 6. P. 449. URL: https://doi.org/10.3390/biomimetics8060449.
Fake news detection on social media / K. Shu et al. ACM SIGKDD explorations newsletter. 2017. Vol. 19, no.1. P. 22–36. URL: https://doi.org/10.1145/3137597.3137600 .
Shevchenko O., Bondarchuk A., Polonevych O., Zhurakovskyi B., Korshun N. (2021) Methods of the objects identification and recognition research in the networks with the IoT concept support. Workshop on Cybersecurity Providing in Information and Telecommunication Systems, (pp. 197–209)
Moshenchenko, M., Zhurakovskyi, B., Poltorak, V., Bondarchuk, A., Korshun, N. (2021) Optimization Algorithms of Smart City Wireless Sensor Network Control. Workshop on Cybersecurity Providing in Information and Telecommunication Systems, (pp. 32–42)

Formation of requirements for the architecture and functions of cyber security monitoring systems

Thu, 23 Jan 2025 20:14:09 +0000

Abstract. The article addresses the challenges and tasks of formulating requirements for the architecture and functions of cybersecurity monitoring systems. These systems, in modern information and communication systems, serve two essential purposes. On one hand, they play a critical role in collecting and analyzing data related to cyberattacks and their timely detection. On the other hand, they act as a tool for studying vulnerabilities and attack conditions during cyber incident investigations to determine adequate organizational and technical countermeasures and ensure their prompt implementation. Based on statistical decision theory, the paper proposes two critical characteristics of cybersecurity monitoring systems: selectivity and sensitivity. The selectivity of a monitoring system is defined by the probability of a Type I error when distinguishing between two hypotheses: H₀, corresponding to the normal functioning state of an automated system, and H₁, representing a scenario where a cyberattack is being executed. Sensitivity is defined by the probability of a Type II error, where H₀ is considered correct, despite the actual validity of its alternative. Another significant quantitative metric identified is the response time delay to events within an automated system, which directly impacts the operational efficiency of cybersecurity management. Given the influence of decisions made by the monitoring system on an organization's overall cybersecurity state, the system must ensure the confidentiality and integrity of the information accumulated, processed, and stored. The paper also proposes additional characteristics of monitoring systems that are crucial for their evaluation and certification.

Keywords: cybersecurity monitoring system, cybersecurity, threat, information protection, confidentiality, integrity, SIEM, LMS.

References

Smirnova, T., Konstantynova, L., Konoplitska-Slobodeniuk, O., Kozlov, Y., Kravchuk, O., Kozirova, N., & Smirnov, O. (2024). Study of the Current State of SIEM Systems. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 1(25), 6–18. https://doi.org/10.28925/2663-4023.2024.25.618
Accorsi, R. (2009). Log data as digital evidence: What secure logging protocols have to offer? 2009 33rd Annual IEEE International Computer Software and Applications Conference, 2, 398–403. doi:10.1109/COMPSAC.2009.166
Kusaka et al. (2014) Log Management System and Program. United States Patent US 8,738,625 B2. 47p.
Holik, F. et al. (2015) The deployment of security information and event management in cloud infrastructure. 25th International Conference Radioelektronika. 399-404. ISBN 978-1-47998117-5
Safarzadeh, M et al. (2019) A Novel and Comprehensive Evaluation Methodology for SIEM. Information Security Practice and Experience, ISPEC 2019 Vol. 1879. 476-488.
Gonzalez-Granadillo, G. (2021) Security Information and Event Management (SIEM): Analysis, Trends, and Usage in Critical Infrastructures. SENSORS. 21(14). AN 4759.
Gibert, D., Mateu, C., & Planes, J. (2020). The Rise of Machine Learning for Detection and Response: SIEM Evolution. ACM Computing Surveys, 53(4), 85-105. doi:10.1145/3409573
Chinenye Cordelia Nnamani (2024) Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response. International Journal of Education, Management, and Technology. 2(3), 2024. 268-286.
Mohammad Habibullah Rakib et al. (2022) A Blockchain-Enabled Scalable Network Log Management Journal of Computer Science, 18 (6): 496.508 DOI:10.3844/jcssp.2022.496.508
Sheeraz, M (2023) Effective Security Monitoring Using Efficient SIEM Architecture. Human-Centric Computing аnd Information Vol.13 AN 23. DOI:10.22967/HCIS.2023.13.023
A. R. (2024) Integrating Predictive Analytics with SIEM for Enhanced Threat Detection. Indian Journal of Information Technology. 4(1), 2024, 1-11. ISSN Online: 2251-2813
Conti, M., Dragoni, N., & Lesyk, V. (2016). A Survey of Man in the Middle Attacks. IEEE Communications Surveys & Tutorials, 18(3), 2027-2051. DOI:10.1109/COMST.2016.2548426
Hulak, H. M., Zhiltsov, O. B., Kyrychok, R. V., Korshun, N. V., & Skladannyi, P. M. (2024). Information and cyber security of the enterprise. Textbook. Lviv: Publisher Marchenko T. V.

Stabilization of autonomous program flight of uavs under conditions of parametric uncertainty

Mon, 06 Jan 2025 11:10:30 +0000

The article is devoted to the development of a method for correcting feedback loops of a closed-loop dynamic system with parametric uncertainty, which provides stabilization of the UAV program movement with given indicators of the quality of transient processes. The synthesis of a robust controller is based on the concept of admissibility, which uses as an assessment the primary indicators of the quality of transient processes, such as transition time, dynamic and static accuracy, and others. The results of modeling the dynamics of UAV movement with parametric uncertainty showed that the transient processes in the stabilization system correspond to the given indicators of the quality of transient processes and are guaranteed to ensure the stability of the dynamics of UAV movement. In real conditions, the parameters of large aircrafttype UAVs and the disturbances acting on them may be known inaccurately or determined ambiguously. Information about parametric uncertainty may be limited only to the boundaries of the areas of parameter change, given, for example, by technical tolerances. In such conditions, one has to deal with a family of dynamic systems, the parameters of which can take on any values within the given limits. Thus, the problem of analyzing and ensuring the stability of systems with uncertainty occupies one of the central places in the theory and practice of control. The article is devoted to the development of a method for correcting feedback loops of a closed-loop dynamic system with parametric uncertainty, which provides stabilization of the UAV program movement with given indicators of the quality of transient processes. The synthesis of a robust controller is based on the concept of admissibility, which uses as an assessment the primary indicators of the quality of transient processes, such as transition time, dynamic and static accuracy, and others. The results of modeling the dynamics of UAV movement with parametric uncertainty showed that the transient processes in the stabilization system correspond to the given indicators of the quality of transient processes and are guaranteed to ensure the stability of the dynamics of UAV movement. Keywords: UAV, linearized model of movement dynamics, parametric uncertainty, method for correcting feedback loops, dynamic quality indicators, stability of UAV movement

References:

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Miklukha,V., Khimchyk N. Optimization of the flight trajectory of an unmanned aerial vehicle / The trajectory of science, vol. 3, no. 9, 2017. – P.1009-1015. http://dx.doi.org/10.22178/pos.26-5.
Солдатова М.О. Автоматизація процесу стабілізації програмного руху безпілотного літального апарату (БПЛА)). – Рукопис. Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.07 – Автоматизація процесів управління. Київ, 2019. – 171с.
Zhou K., Doyle J. C., Glover K. Robust and optimal control. Englewood: Prentice Hall, 1996. 596 p.
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Aliluiko, R. Ruska Robust stability and evaluation of the quality functional for linear control systems with matrix uncertainty / Scientific Journal of the Ternopil National Technical University, № 3 (99), 2020. – P.55-64. https://doi.org/10.33108/visnyk_tntu2020.03
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Omorov T. T. The principle of guaranteed dynamics in the theory of control systems. Book 1. Bishkek. 2001. – 150 p.
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Мельник К.В. Технологія μ-синтезу у завданнях управління польотом. – Рукопис. Дисертація на здобуття наукового ступеня кандидата технічних наук зі спеціальності 05.13.12 – Системи автоматизації проектних робіт. - Національний авіаційний університет, Київ, 2009.

The method of improving the accuracy of passive finding with the help of automatic calibration of antenna systems

Mon, 06 Jan 2025 11:16:31 +0000

The article focuses on improving the accuracy and fault tolerance of the passive direction-finding method through antenna calibration. Passive direction-finding systems are widely used in radiomonitoring, electronic warfare, navigation, civilian and military communication systems, as well as in electronic intelligence. Their effectiveness depends on the accuracy of operation and resilience to external factors, including interference and noise. The paper examines the amplitude comparison method, which is one of the most common approaches for determining the direction of a signal source. This method is based on measuring the amplitudes of signals received by several antennas positioned at specific angles and subsequently analyzing these values. However, due to external factors such as inhomogeneities in the signal propagation medium or mutual antenna interference, errors may occur, reducing system accuracy. The study proposes an antenna calibration methodology to minimize the influence of these factors. The process of automatic calibration is described, including the use of a standard horn antenna, a spectrum analyzer, and a high-frequency signal generator. An algorithm for collecting and processing amplitude characteristic data is proposed, which allows for the construction of a calibration table that normalizes amplitude ratios and ensures accurate determination of the signal source direction. The proposed approach enhances the reliability of passive direction-finding systems and ensures their efficient operation under the influence of external factors, which is critically important in both military and civilian applications.

Keywords: passive direction finding, antenna systems, automation, calibration, information technology.

References

Lee J. -H., Kim J. -K., Ryu H. -K., Park Y. -J. Multiple Array Spacings for an Interferometer Direction Finder With High Direction-Finding Accuracy in a Wide Range of Frequencies. IEEE Antennas and Wireless Propagation Letters. 2018, Vol. 17, no. 4, Pp. 563-566.
Li J., Zhang Q., Deng W., Tang Y., Zhang X., Wu Q. Source Direction Finding and Direct Localization Exploiting UAV Array With Unknown Gain-Phase Errors. IEEE Internet of Things Journal. 2022, Vol. 9, no. 21, Pp. 21561-21569.
He W., Zhou Q., Zhang X., Zhao Y., Li B., Zhang L. Research on direction finding of UAV coherent signals based on uniform circular array. 2022 18th International Conference on Computational Intelligence and Security (CIS): Proceedings 18th International Conference on Computational Intelligence and Security (CIS) (Chengdu 16-18 December 2022). China, 2022, Pp. 445-447.
Sklar J. R., Ward J. 11 Copy: Steering Vector Methods. Modern HF Signal Detection and Direction Finding. MIT Press, 2018. Pp.269-288.
Ren K. Direction Finding Using a Single Antenna With Blade Modulation. IEEE Antennas and Wireless Propagation Letters. 2022. Vol. 21, no. 5, Pp. 873-877.
Sklar J. R., Ward J. 9 Direction Finding Techniques for HF Applications. Modern HF Signal Detection and Direction Finding, MIT Press, 2018. Pp.217-248.
Tetley L., Calcutt D. Chapter 10 - Radio direction finding Electronic Navigation Systems (Third Edition). Elsevier Press, 2001, Pp. 346-368.
Boiko, J., Polıkarovskykh, O., Tkachuk, V., Yehoshyna, H., Karpova, L. Design Concepts for Mobile Computing Direction Finding Systems. Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies. Springer, 2023. Vol. 166. Pp. 89–107.
Boiko, J., Polikarovskykh, O., Tkachuk, V. Development and modeling of the antenna system the direction finder unmanned aerial vehicle. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska. 2023, 13(1), Pp. 26-32.
Sonnenberg G.J. Chapter 3 Direction finding Book: Radar and Electronic Navigation (Sixth Edition). Butterworth & Co. (Publishers) Ltd. Published by Elsevier Ltd, 1988. Pp. 93-126.
Zhou W., Zhou Y. Research on Interferometer Direction Finding Technology Based on Digital Beam forming. 2022 7th International Conference on Signal and Image Processing (ICSIP): Proceedings 7th International Conference on Signal and Image Processing (ICSIP) (Suzhou, 20-22 July 2022). China, 2022, Pp. 54-58.

Information technologies for visualization and data processing in the sphere of geospatial intelligence

Mon, 06 Jan 2025 11:21:51 +0000

Current research demonstrates the great potential of the integration of big data technologies and artificial intelligence for automated decoding of satellite images, monitoring of landscape changes, detection of violations and prediction of man-made risks. The use of machine learning algorithms in the visualization of geodata significantly accelerates the analysis of information of large arrays, which is critically important for environmental monitoring during military operations and rapid recovery of the territory. The detection of the state of ground objects is significantly improved by the use of multispectral and hyperspectral images, in particular from unmanned aerial vehicles, after their differentiation allows the creation of high-quality information products. However, hyperspectral images from space height still do not provide the necessary level of detail for operational-tactical intelligence. Research highlights the effectiveness of integrating big data and artificial intelligence for automatically deciphering satellite images, monitoring landscape changes, and assessing man-made risks. The use of machine learning algorithms accelerates the processing of large sets of data, which is a special place for environmental monitoring during military operations and territory recovery. Increasing the accuracy of detecting the state of objects is achieved with the help of multispectral and hyperspectral images, especially obtained from drones, and the latest images from the space platform still lack sufficient detail for operational tasks.

Keywords: information technologies, artificial intelligence, spectral channels, remote methods. machine learning algorithm, visualization of geodata, decoding of satellite images

References

V. Trysnyuk, V. Prystupa, T. Trysnyuk, V. Vasylenko, A. Kurylo. Comprehensive environmental monitoring based on aerospace and ground research data. Наживо. XIX thInternationalConference “Geoinformatics: TheoreticalandAppliedAspects”. Geoinformatics 2020. 11-14 May 2020, Kyiv, Ukraine.DOI: https://doi.org/10.3997/2214-4609.2020geo066
Krasovsky G.Ya., Trofymchuk O.M., Kreta D.L., Klymenko V.I. Ponomarenko I.G., Sukhodubov O.O. Synthesis of cartographic models of land pollution by man-made dust using space images // Ecology and resources. - K.: IPNB, 2005. - No. 12. - P. 37 - 55. 2. Trofymchuk, O., Kalyukh, Y., Hlebchuk, H. [2013] Mathematical and GIS-modeling of landslides in kharkiv region of Ukraine. LandslideScienceandPractice: Spatial Analysis and Modelling. – Springer, Berlin, Heidelberg. 347- 352.
Mashkov O.A., Trysnyuk V.M.; Mamchur Y.V., Zhukauskas S.V., Nigorodova S.A., Kurylo A.V. A new approach to the synthesis of restorative control for remotely piloted aerial vehicles for environmental monitoring. Environmental safety and balanced resource use: science and technology. journal - Ivano-Frankivsk: Symphony forte. - 2019. No. 1. (19) 2019. p. - 69-77.
Zaitsev S. V. Method of estimating reliability of information transmission in wireless networks channels increase in noise and interference / S. V. Zaitsev // International Journal «Information Models and Analyses». – Sofia : ITHEA, 2015. – Vol. 4 (1). – P. 87 – 99.
V. Trysnyuk, T. Trysnyuk, V. Okhariev, V. Shumeiko, A. Nikitin. Cartographic Models of Dniester River Basin Probable Flooding Сentrul Universitar Nord Din Bala Mare - UTPRESS ISSN 1582-0548, №1,2018 С.61-67.
Bondarchuk A.P., Zhebka V.V. Protection of a heterogeneous telecommunication network from the influence of destabilizing factors // Telecommunications and Information Technologies, 2023. No. 1 (78), pp. 4-16.
Shevchenko O., Bondarchuk A., Polonevych O., Zhurakovskyi B., Korshun N. (2021) Methods of the objects identification and recognition research in the networks with the IoT concept support. Workshop on Cybersecurity Providing in Information and Telecommunication Systems, (pp. 197–209)

Integrration of a camera into an FDM printing system to improve print quality

Mon, 06 Jan 2025 11:26:57 +0000

This article explores the possibilities of improving the quality of FDM (Fused Deposition Modeling) printing through the integration of a camera into the print process control system. FDM is one of the most widely used methods in additive manufacturing due to its accessibility and broad range of applications. However, the printing process is often accompanied by various defects such as layer shifting, under-extrusion, overheating or underheating of the material, and adhesion issues with the print bed. These problems not only degrade the quality of the final product but also lead to increased time and material costs. Integrating a camera into the FDM printing system offers a solution for early defect detection during the printing process, enabling automated adjustments of printing parameters in real time. Cameras allow for continuous monitoring of the print process at each stage, analyzing material layers, their alignment, and surface quality. Image processing algorithms and machine learning techniques facilitate the rapid and accurate identification of defects, predict potential deviations, and automatically modify print parameters to prevent quality deterioration. In addition to automatic parameter correction, the system can provide users with specific recommendations for improving equipment settings if issues are detected that cannot be addressed automatically. This approach significantly enhances the precision and reliability of the printing process, reducing the number of defective products and improving production efficiency. The article also discusses the future prospects of this technology, including the use of more powerful machine learning algorithms to increase the accuracy of defect analysis and prediction, the integration of cloud technologies for remote monitoring of printing processes, and the use of advanced camera types (such as infrared or 3D cameras) for even more precise quality control. Thus, integrating a camera into the FDM printing system represents a crucial step in the evolution of additive manufacturing technologies, offering significant improvements in the quality of printed products and optimization of the production process.

Keywords: FDM printing, additive technologies, camera integration, image processing, machine learning, quality control, automation, print defects, 3D printing, print parameter correction.

References

Boschetto, A., Bottini, L., & Veniali, F. (2013). Surface roughness prediction in fused deposition modeling by neural networks. The International Journal of Advanced Manufacturing Technology, 67(9), 2727-2742.
Valino, A. D., et al. (2019). Fused Deposition Modeling 3D printing: Effect of printing parameters on mechanical properties of wood PLA. Polymers, 11(4), 755.
Górski, F., et al. (2021). Application of artificial intelligence algorithms for quality control in 3D printing. Procedia CIRP, 104, 1695-1700.
Wang, T., et al. (2022). Computer vision-based defect detection for FDM 3D printing using convolutional neural networks. Journal of Manufacturing Systems, 63, 11-21.
Ahn, S., et al. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248-257.
Coogan, T., & Kazmer, D. (2017). In-line rheological monitoring of fused deposition modeling. Journal of Rheology, 61(1), 141-152.
Rahman, A. U., et al. (2021). Recent trends in 3D printing: A review on its applications and research. Materials Today: Proceedings, 47(9), 152-162. doi:10.1016/j.matpr.2021.09.172
Paul, J., et al. (2020). Fused deposition modeling-based additive manufacturing: Influence of printing parameters. 3D Printing and Additive Manufacturing, 7(2), 63-78.
Tian, X., et al. (2021). Overview of recent advances in fused filament fabrication 3D printing technology. Polymer Reviews, 61(4), 679-746.
Yang, J., et al. (2023). AI-driven defect detection and correction in additive manufacturing. International Journal of Mechanical Sciences, 241, 107926.

Modeling the user’s security profile to determine his potential vulnerability to social engineering attacks

Запорожченко Михайло Михайлович (Zaporozhchenko Mykhailo) — Mon, 06 Jan 2025 11:30:05 +0000

The evolving threats of social engineering attacks (SEA) in the context of the active use of digital technologies impose new requirements for the protection of corporate information systems (IS). The article presents the mathematical model of the user's security profile, which is designed to evaluate his potential vulnerability to SEA. The proposed model is based on the integration of four key factors psychological, organizational, technical and informational impact, which enables a comprehensive risk analysis. The existing approaches to assessing user vulnerability have been reviewed and their limitations have been identified, in particular, the limited consideration of the complex interaction of factors. The proposed model solves these limitations, allowing to assess the vulnerability of users in a dynamic environment, taking into account changing external environment and users' individual characteristics. The approach is based on modeling the SEA process, divided into three key stages: delivery of attacking content, user interaction with this content, and avoidance of attack detection. For each stage, the corresponding mathematical dependencies have been developed that take into account the interaction of these factors. The results of the modeling allow to identify groups of vulnerable users and critical stages at which a user or a system is most vulnerable to attacks. The proposed approach also allows to adapt protection measures to the real conditions of corporate environments, ensuring consistency between risk assessment and protection needs. The model can be used to develop targeted SEA prevention measures and improve the overall state of information security. Thus, the proposed user security profile model is a universal tool for predicting SEA risks in corporate IS. It provides the ability to analyze and prevent attacks by quantifying individual and external factors that determine user behavior. In addition, the model allows to optimize the development of protection strategies, ensuring their flexibility and adaptability to changes in the information environment. This ensures a systematic approach to risk assessment and minimizes the vulnerability of IS to social engineering threats.

Keywords: social engineering risks, information security, corporate systems, information impact, mathematical modeling, adaptive protection, vulnerability assessment, risk prediction.

References

Albladi S., Weir G. Predicting individuals’ vulnerability to social engineering in social networks. Cybersecurity. 2020. № 3. 7.
Ye Z., Guo Y., Ju A., Wei F., Zhang R., Ma J. A risk analysis framework for social engineering attack based on user profiling. Journal of Organizational and End User Computing. 2020. Vol. 32, № 3. Р. 37-49.
Huseynov F., Ozdenizci Kose B. Using machine learning algorithms to predict individuals’ tendency to be victim of social engineering attacks. Information Development. 2024. Vol. 40, № 2. Р. 298-318.
Бохонько О., Лисенко С. Методи виявлення кібератак соціальної інженерії. Вісник Хмельницького національного університету. Технічні науки. 2023. Том 327, № 5(2). С. 231-236.
Aijaz M., Nazir M. Modelling and analysis of social engineering threats using the attack tree and the Markov model. International Journal of Information Technology. 2024. № 16. P. 1231-1238.
Fakhouri H.N., Alhadidi B., Omar K., Makhadmeh S.N., Hamad F., Halalsheh N.Z. AI-driven solutions for social engineering attacks: detection, prevention, and response. 2024 2nd International Conference on Cyber Resilience (ICCR). Dubai, United Arab Emirates. 2024. P. 1-8.
Wang Z., Sun L., Zhu H. Defining Social Engineering in Cybersecurity. IEEE Access. 2020. Vol. 8, P. 85094-85115.
Hadnagy C. Social engineering. The science of human hacking. Indiana: John Wiley & Sons, Inc. 2018.
Siponen M., Vance A. Neutralization: New Insights into the Problem of Employee Information Systems Security Policy Violations. MIS Quarterly. 2010. Vol. 34, № 3. Р. 487-502.
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Methodology for forecasting user mobility in 5G networks

Mon, 06 Jan 2025 11:35:48 +0000

The article is devoted to the development of a methodology for predicting user mobility in 5G networks using machine learning, spatiotemporal correlation and mathematical modeling. The main goal of the work is to increase the accuracy of predicting user movements, taking into account the specifics of 5G networks, such as high data rates, limited base station resources and complex network topology. The article presents a detailed formalization of the problem, where the user's mobility is described through a discrete set of states corresponding to its position in space and time. An objective function is proposed to minimize the discrepancy between the predicted and real probabilities of user movement. The spatiotemporal model takes into account physical, topological and network constraints, such as movement speed, availability of transitions between states and base station capacity. The methodology is based on the use of recurrent neural networks (RNN), which allow modeling dependencies in time and space. The model training process includes parameter optimization using stochastic gradient descent with regularization, which improves the algorithm's convergence. Metrics such as Accuracy, F1-score, and Log-loss are used to assess the prediction accuracy. The research results confirm the effectiveness of the method in scenarios with high user movement speed and complex network structure. Integration of the model into the network management system allows for dynamic optimization of resource allocation, reduction of congestion probability, and improvement of service quality. Future research prospects include adaptation of the method for next-generation networks and integration of new data sources, such as information from satellite systems.

Keywords: 5G network, information system, mobility, prediction methods, machine learning methods, neural networks, information technology.

References

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Overview of telecommunications network data collection methods by probe

Сагайдак Віктор Анатолійович (Sahaidak V.A.) — Mon, 06 Jan 2025 11:41:08 +0000

In this article overview was provided on two systems for traffic collection in real time. These systems are Gigamon fabric solution and Huawei NetProbe. Gigamon fabric solution performs information collection/filtering/enrichment. Following solution supports SS7, IP, 3G, LTE. Information from network elements can be collected by network TAPs (splitters) or traffic port mirroring. Data processing stages with description of each function and used devices description were provided. Huawei NetProbe is used for decoding and creation of call detailed records (CDRs), storing raw signaling data and real time session tracing. This system can recognize more than 1300 protocols and applications by self-developed by Huawei Service Awareness Engine. NetProbe supports traffic collection from NGN, GSM (CS and PS), UMTS (CS and PS), LTE, IMS. DPI (Deep protocol inspection) base, used by Probe to recognize data, is released periodically to support latest protocols. In case if protocol is missing in DPI, it can be configured. System workflow was described. Detailed overview was provided on secondary devices used by both systems and risks were described during usage on telecommunications network. Example of network was provided and analyzed changes that should be done for reviewed data collection method, depending on network elements change, update or new site deployment. Following conclusions were made: Real time data collection provides a lot of pros, but also requires careful investigation before implementation of one or another method. Port mirroring can duplicate information without additional hardware installation, but can have significant impact on network element performance. TAPs can duplicate traffic with cost of signal power on both ends. Active TAP doesn’t have such issue, but power outage on such device can cause service loss; Every network change like hardware update, vendor change or new site deployment should be done with thought that data collection should be also updated. Nether less, device configuration should be changed in order to clean duplicates in messages. It increases cost and time for operation and maintenance; Data collection by Probe can be excessive due to realization, required processing resources and amount of collected information, which most probably will never be used. In such case, if only one customer from company requires it, it is better to look for cheaper and easy to use sources of data.

Keywords: GTP, CS network, PS network, information technology, VNF, LTE, IMS, SS7, VoIP, IP, Big data.

References

GigaVUE-FM Overview. GigaVUE 5.8 Online Documentation. URL: http://surl.li/zrdava
wangshupeng. SC1002 HUAWEI SmartCare SEQ Analyst & NetProbe Technical Slides V2.4 | PDF | Service Level Agreement | Websites. Scribd. URL: http://surl.li/vvbtbq
Understanding international telecoms fraud. Network-Level Intelligence for Observability Tools | Gigamon. URL: http://surl.li/joztlh
Example for Configuring Local Port Mirroring (1:1 Mirroring) - S600-E Series Switches Typical Configuration Examples. Huawei. URL: http://surl.li/qfdhia
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