The article presents an analysis of the robustness of an authentication scheme based on zero watermarking. The study examines a two-factor authentication scheme that uses "knowledge of something" (a password) and "possession of something" (a digital RGB image) as its factors. The zero watermarking algorithm chosen is based on DWT and K-means transformations, with additional use of the Swish function. The analysis is conducted by considering the theoretical complexity of the algorithm assuming the adversary knows its parameters, such as the password, the hash of the password, the image, the reference watermark, the transformation result, and other parameters. Previous studies have shown a high theoretical robustness of the scheme, which relies on the complexity of the password and the dimensionality of the image. For large image sizes (512×512 pixels and above), a relatively high level of cryptographic resistance is achieved. However, this robustness is not formally proven, and the actual strength may be significantly lower due to the specifics of the images and transformations, which can introduce additional vulnerabilities. The algorithm is subject to a relatively high rate of collision, associated with digital image transformations and matrix multiplications, which weakens its resistance. Authentication schemes and zero watermarking algorithms require further research, formal proof of cryptographic properties, and methods for integration into access control systems, as they can provide a high level of authentication robustness in systems with high noise levels. Additionally, the convenience and low cost of such schemes give them an advantage over other authentication methods. The study provides recommendations for improving the potential characteristics of the algorithm.
Open access
Advanced Steganography and Watermarking Techniques
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
Oleg Kravets, Ali Husein, Diana Getmanskaia, Mustafa Al-Imari · 7 authors
The article discussed an automata model of the functioning process of a system with distributed ledger technology based on a blockchain. The goal is to develop a special mathematical system with a distributed ledger based on a blockchain, taking into account the specifics of implementing algorithms for mutual information coordination, the possibility of combining individual nodes into groups, and implementing alternative strategies based on the development of appropriate models and algorithms that ensure increased stability in their operation. Automata theory was used, theory and methods of mutual coordination were applied, and the mechanism of the functioning process of a system with a distributed register was implemented. An automata model has been obtained and investigated. Thus, an automata model of the distributed ledger technology blockchain system node functioning process has been developed, which differs from the node representation by a finite state machine with a variable structure and a linear tactic with the possibility of implementing non-standard functions: the formation of a branch of processed data and a temporary blockage attack, and provides for obtaining the dependence of the sequence of changing the node's behaviour strategy options on the conditions of the environment it interacts with.
Farhad Asgarov, Fatih Said Duran, Namig Samadov, Şerif Bahtıyar
Cite this article as: F. Asgarov, F. S. Duran, N. Samadov and Ş. Bahtiyar, “Blockchain based ownership and DNS configuration with ethereum rollups,”Electrica, 25, 0051, doi: 10.5152/electrica.2025.25005.
The rapid development of decentralized technologies and blockchain is transforming the methods of authentication, data management and the implementation of digital human rights, which actualizes the need to form a new identity paradigm based on user autonomy and trustful interaction without intermediaries. The purpose of this article is to substantiate self-sovereign identity as the foundation of trust and digital asset management within the Web3 ecosystem. The research methodology combines comparative legal and formal-dogmatic analysis, structural-functional modeling of the three-way interaction among issuer, holder, and verifier, as well as a problem-oriented review of the technical standards and practices of early platforms (Sovrin, uPort). It is demonstrated that the emergence of self-sovereign identity is a natural response to the shortcomings of centralized and federated identification models in Web 2.0 (OAuth 2.0, OpenID Connect): dependence on providers, concentration of leakage risks, and inability to disclose attributes selectively. The article reveals the mechanism of trust formation in the self-sovereign identity system, which is based on a three-party model of interaction between the issuer, the holder and the verifier; in this model, data authenticity is ensured using cryptographic verifiability through decentralized identifiers and verifiable credentials, which allows minimizing the participation of intermediaries, reducing the surface of possible attacks and guaranteeing the autonomy of the data subject in the process of managing their own digital identity. The key principles of self-sovereign identity (control, availability, transparency, minimization of disclosure, portability, security/resilience, and consent) are systematized, and their applied role in forming a «trust architecture» in Web3 (DAO, DeFi, NFT) is demonstrated. The study revealed a regulatory asymmetry between the technological development of self-sovereign identity systems and the level of their legal regulation. For Ukraine, key regulatory gaps have been specified that hinder the implementation of self-sovereign identity systems and limit the possibility of integrating Ukrainian e-government systems into the international Web3 space: the legislation lacks definitions of the terms «self-sovereign identity» and «decentralized identifier», which is why these concepts have no legal status in Ukraine; the current legal framework for electronic identification and personal data protection is incompatible with the principles of decentralization, self-control, and minimization of information disclosure, which underlie the SSI model. The practical significance of the results lies in the proposed holistic legal and technical framework for developing Web3 trust services, which enables the design of interoperable and secure processes for managing digital assets, prioritizing personal sovereignty over data.
Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within the UAV cluster, is responsible for coordinating and managing various tasks within the cluster. When the ACA undergoes fault recovery, a handover operation is required, during which the ACA must re-authenticate its identity with the UAV cluster and re-establish secure communication. However, traditional, centralized identity authentication and ACA handover mechanisms face security risks such as single points of failure and man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading to reduced handover efficiency and increased authentication latency. To address these challenges, this paper proposes a mathematically structured dual-chain framework that utilizes a distributed ledger to decouple the management of identity and authentication information. We formalize the ACA handover process using cryptographic primitives and accumulator functions and validate its security through BAN logic. Furthermore, we conduct quantitative analyses of key performance metrics, including time complexity and communication overhead. The experimental results demonstrate that the proposed approach ensures secure handover while significantly reducing computational burden. The framework also exhibits strong scalability, making it well-suited for large-scale UAV cluster networks.
The development of high-load computing systems in modern information environments represents a critical aspect of technological progress, necessitating the creation of innovative approaches to cybersecurity. The increasing intensity of data exchange, the complexity of computational processes, and the growing number of interacting nodes pose significant challenges to traditional information security methods. Classical centralized security models are gradually losing their effectiveness due to the high risk of data processing center compromise, the vulnerability to denial-of-service (DDoS) attacks, unauthorized access, and system exploits. These risks emphasize the necessity of implementing decentralized security mechanisms that can withstand emerging cyber threats and ensure the reliability of high-load computing infrastructures. This article presents a conceptual approach to enhancing the cybersecurity of high-load computing systems through the integration of blockchain technology and smart contracts. A comprehensive analysis of current threats and risks inherent in such systems has been conducted, along with an investigation into the efficiency of smart contracts in user authentication, access control, data verification, and attack prevention. Particular attention is given to the advantages of decentralized security solutions, including the elimination of single points of failure, the enhancement of transparency in security processes, and the automation of control mechanisms. The study also evaluates the resilience of blockchain-based security frameworks in mitigating both internal and external cyber threats. The proposed architectural model leverages smart contracts for managing access to computing resources, verifying transaction integrity, and minimizing the impact of external threats. The analysis of recent research in blockchain technologies and their application in high-load environments provides insights into the feasibility and advantages of such an approach. By utilizing smart contracts, it becomes possible to automate security procedures, reduce reliance on centralized authentication servers, and ensure that system interactions remain tamper-proof and resistant to adversarial attacks. The research findings indicate that integrating smart contracts into high-load computing systems enhances cybersecurity by automating data verification processes, eliminating intermediaries in transactions, and strengthening resilience against attacks. Furthermore, the study outlines promising directions for future research, including the optimization of smart contract execution mechanisms to reduce computational overhead and the integration of blockchain-based solutions into hybrid security models that combine decentralized and centralized approaches. This approach offers a strategic pathway for developing robust, scalable, and resilient cybersecurity frameworks tailored to the needs of high-performance computing infrastructures.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
У статті проаналізовано можливості платформи Ethereum для розроблення та впровадження смарт-контрактів в інформаційній системі обліку продажу зброї. Розглянуто архітектуру платформи, механізми забезпечення прозорості, безпеки та достовірності даних у процесах купівлі-продажу. Особлива увага приділена аналізу ключових компонентів платформи, таких як Ethereum Virtual Machine (EVM) та механізм консенсусу Proof of Stake, які забезпечують децентралізовану та захищену основу для роботи смарт-контрактів. Досліджено потенціал смарт-контрактів для автоматизації ключових етапів обліку, включаючи перевірку дозволів на придбання зброї, реєстрацію угод, контроль дотримання законодавчих норм і відстеження змін у даних у реальному масштабі часу. Визначено переваги використання блокчейн-технологій, серед яких — незмінність записів, доступність для аудиту в режимі реального часу, підвищена прозорість операцій і мінімізація людського фактору. Окремо підкреслено, що інтеграція блокчейну дозволяє створити надійні механізми запобігання несанкціонованому доступу, фальсифікації даних і шахрайству. Розглянуто приклади реалізації смарт-контрактів у системі обліку продажу зброї та описано, як автоматизація цих процесів може сприяти зменшенню адміністративного навантаження. Водночас у статті висвітлено ключові виклики впровадження системи, включаючи питання масштабованості мережі, забезпечення конфіденційності персональних даних користувачів, адаптацію існуючих законодавчих норм до блокчейн-рішень і підвищення рівня довіри між учасниками системи. Запропоновано можливі напрями подальших досліджень, зокрема щодо інтеграції смарт-контрактів з іншими цифровими технологіями для забезпечення комплексного підходу до обліку та моніторингу. Результати роботи демонструють високий потенціал використання платформи Ethereum для підвищення ефективності та прозорості процесів у сфері контролю обігу зброї. Інтеграція смарт-контрактів із сучасними інформаційними системами обліку відкриває нові можливості для створення надійних і безпечних рішень, які відповідають викликам цифрової епохи.
In the rapidly evolving digital technology landscape, blockchain emerges as a pivotal innovation with the potential to revolutionize industries far beyond its original application in cryptocurrencies like Bitcoin. This article explores the critical role of cryptography in decentralized information networks, emphasizing its importance in ensuring the integrity, confidentiality, and authenticity of digital transactions. It examines the mathematical foundations underlying cryptographic techniques, including elliptic curve cryptography and hash functions, and discusses their application in consensus protocols such as Proof-of-Work. Furthermore, the paper addresses the challenges and potential vulnerabilities posed by quantum computing to current cryptographic standards. By providing a comprehensive overview of both theoretical and practical aspects of cryptography in blockchain technology, this study aims to enlighten readers on the robust security measures essential for maintaining trust and security in decentralized systems. Moreover, the discussion extends to the implications of cryptographic advancements for various sectors such as healthcare, finance, and public administration, highlighting how blockchain enhances transparency and security in these fields. This article underscores the urgency of advancing cryptographic research to address emerging threats and ensure the resilience of blockchain systems against future technological developments. The findings emphasize the need for ongoing innovation in cryptographic methods to safeguard the integrity of decentralized networks in an era of increasing digital interconnectivity.
The pseudonymous nature of Bitcoin continually generates interest in services aimed at enhancing transaction anonymity. One such solution is the use of Bitcoin mixing services, commonly referred to as mixers, which are employed to increase user privacy. However, their use can be controversial, as while they serve to enhance financial privacy, they can also be exploited for illicit purposes. One of the challenges faced by law enforcement is identifying suspicious Bitcoin addresses. The purpose of this article is to examine the behavior of selected cryptocurrency mixers as a foundation for future research in this area.
In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.
Current directions of development of information and communication technologies and control tools 114 ПРОБЛЕМНІ ПИТАННЯ ЗАСТОСУВАННЯ ГОМОМОРФНОГО ШИФРУВАННЯ Гущин Б.-Д.І.Харківський національний університет радіоелектроніки, Харків, Україна Гомоморфне шифрування (Homomorphic Encryption, HE) -технологія, що дозволяє виконувати обчислення над зашифрованими даними без їх розшифрування [1-3].Застосування гомоморфного шифрування відкриває великі можливості для обробки зашифрованих даних без їх розшифрування в багатьох сферах, де критично важливо зберігати конфіденційність інформації.. Однак, попри потенціал, існує низка проблемних питань, які стримують широке впровадження цієї технології в практичні системи безпеки та обробки даних.Метою доповіді є аналіз проблемних питань при практичному застосуванні гомоморфного шифрування.В роботі розглянуті ключові сучасні сфери застосування HE [1-3].1. Хмарні обчислення (Cloud Computing), обробка зашифрованих даних на стороні постачальника хмарних послуг без розкриття вмісту.2. В медицині та біоінформатиці захищене зберігання та аналіз медичних записів пацієнтів, генетичної інформації (DНК).Використання HE у дослідженнях із багатьма організаціями без обміну розшифрованими даними.3. В фінансових сервісах конфіденційна аналітика для банків, страхових компаній, бірж.Обчислення кредитного рейтингу, оцінка ризиків без доступу до відкритих даних клієнта. Електронне голосування (E-voting).Забезпечення анонімності та перевірності результатів голосування, так як дані не розкриваються, але можуть бути агреговані без розшифрування.5. В Інтернеті речей (IoT) для обробки конфіденційних даних, зібраних з розумних пристроїв, безпосередньо на сервері або у хмарі.Використання в промисловому IoT (IIoT), медичних пристроях, Smart City.6. Захист персональних даних у Big Data, аналітика зашифрованих великих масивів даних з соціальних мереж, телекомів, сервісів для обчислення трендів, поведінкових моделей, не розкриваючи особистості.7. Машинне навчання над зашифрованими даними (Privacy-Preserving ML).Навчання або інференс моделей на даних, які ніколи не розшифровуються.Використання в конфіденційній медичній діагностиці, рекомендаційних системах, фінансах.Працює разом з Federated Learning, Secure Multi-Party Computation (SMPC).8. В юридичних сервісах та державному управлінні для безпечної взаємодії між державними структурами при обробці реєстрів, митних даних, податкових баз, де важлива повна конфіденційність.9. В системах кібербезпеки та цифрової ідентичності.Застосовується в антифрод-системах, аналізі поведінки користувачів без втрати їхньої конфіденційності.Підтримка Zero Knowledge Proofs та протоколів автентифікації.
In this age of always-on connection, it is very important to keep data safe while also protecting user privacy. In today's networks, where data travels through many pathways, such as cloud services and IoT devices, cryptographic algorithms are very important for keeping private data safe. But it's still exceptionally difficult to create beyond any doubt that information is secure without putting people's protection at chance. This conversation goes into detail almost privacy-preserving security strategies, looking at their significance, issues, and other ways to solve them. The objective of privacy-preserving cryptographic strategies is to create beyond any doubt that private information is kept secure whereas still permitting secure contact and computation. To keep data secure from individuals who shouldn't have get to to it, these frameworks utilize diverse sorts of cryptography, like encryption, hashing, and secure multi-party computation (SMPC). Information spills and illicit observing are less likely to happen with these methods because they cover up information at diverse steps of exchange and handling. Indeed in spite of the fact that they may well be useful, privacy-preserving cryptographic strategies have a number of issues. Finding a great blend between client security and information security is one of the most issues. Extreme security measures may offer assistance keep information secure, but they frequently include collecting information in ways that are as well intrusive and abuse people's security. On the other hand, putting as well much accentuation on protection might make security weaker, taking off information open to being abused. Finding a cautious adjust between these competing objectives is key to making cryptographic frameworks that work well. A few potential methods that permit secure information taking care of whereas ensuring security are homomorphic encryption, differential protection, and zero-knowledge proofs. Improvements in hardware-accelerated cryptography and distributed computing tools also make it possible to speed up secure processes and make them more scalable.
V. S. Balatska, R. L. Tkachuk, A. I. Ivanusa, V. I. Yashchuk · 5 authors
Problem. The growing intensity of cyberattacks on state registers and the increasing complexity of insider threats expose the weaknesses of traditional comprehensive information security systems (CISS), particularly the reliance on centralized logging and change verification mechanisms. This reduces audit transparency, complicates the evidential value of incidents, and creates regulatory risks in personal data protection. Purpose. To develop and substantiate a scientific and methodological approach for integrating permissioned blockchain technology into CISS of state registers to enhance resistance to insider actions, ensure transparency of access control, and increase trust in electronic public services. Methods. The study applies a systems analysis of CISS architectures and regulatory requirements, mathematical modeling of data flows, and experimental modeling in a virtualized environment using Hyperledger Fabric as a decentralized logging and verification module. Zero-Knowledge Proofs were applied to preserve transaction confidentiality, and behavioral analytics based on machine learning algorithms were used to detect anomalous activity. Results. A hybrid architecture was proposed in which traditional mechanisms of authentication, access control, and cryptographic protection are reinforced by a distributed event log and consensus-based verification of operations. This integration ensures data immutability and reproducibility of access history, reduces the possibility of hidden record editing by administrators, accelerates the detection of atypical user behavior, and creates a reliable evidential base for auditing. The proposed architecture complies with ISO/IEC 27001 requirements and supports data minimization and accountability principles, facilitating GDPR compliance during personal data processing. Conclusions. Integrating permissioned blockchain into CISS of state registers establishes a new level of trust and controllability of security: it increases audit transparency, mitigates insider risks, and preserves transaction confidentiality. The proposed approach is scalable and suitable for national e-government platforms and interagency data exchange systems.
The article examines the evolution of the concept of Integrated Information Security Systems (IISS) in the context of the digital transformation of the public sector, modernization of the national cybersecurity framework, and harmonization of Ukrainian legislation with international information security standards. The study reveals the relationship between classical approaches to building IISS – based on mandatory certification of technical protection complexes – and the modern paradigm of risk-oriented security management introduced by the new Law of Ukraine No. 4336-IX “On Amendments to Certain Laws of Ukraine on the Protection of Information and Cybersecurity of State Information Resources and Critical Information Infrastructure Objects.” The research emphasizes the shift from a formal certification model to a process-oriented approach based on security profiles, risk management, continuous monitoring, and security auditing. Special attention is devoted to analyzing the potential of blockchain technologies in enhancing the resilience of state information systems against cyberattacks, insider threats, and unauthorized data modifications. The study substantiates the feasibility of using distributed ledgers to ensure the immutability, authenticity, transparency, and accountability of information processes. It is determined that blockchain can serve as an innovative component of the modern IISS architecture, complementing cryptographic protection mechanisms, access control, user activity auditing, and event monitoring. A conceptual model of blockchain integration into the traditional structure of IISS is proposed, forming a new trust ecosystem within state information resources. The combination of technological innovation with the legal requirements of Law No. 4336-IX creates a foundation for improving the effectiveness of the national cybersecurity system. The purpose of the study is to substantiate the scientific, methodological, and technological directions for the modernization of Ukraine’s Integrated Information Security Systems through the integration of blockchain technologies in protecting state information resources in accordance with current legislation and international standards ISO/IEC 27001, ISO/IEC 27701, and GDPR.
Андрій Олександрович Гашко, Андрій Петрович Бондарчук, Максим Петрович Трембовецький, Олександр Ілліч Чумак
The article examines an automated method for verifying the correctness of smart contracts in the Solana blockchain network. The relevance of the research is driven by the growing popularity of Web3 applications and the need to ensure their security, as even minor errors in smart contract code can lead to significant financial losses. The primary goal is to develop an automated verification methodology for smart contracts that can detect vulnerabilities such as the absence of founder rights verification, arithmetic operation errors, and missing transaction check signatures. Using static analysis techniques in the Rust programming language, the authors propose an approach that enables rapid analysis-taking less than three minutes per contract-and automatic generation of reports on identified vulnerabilities. The methodology is based on analyzing external data flows through smart contracts, allowing for the early detection of potential threats. To automate the process, Python and Bash scripts are employed, integrating with cloud services such as Amazon Web Services to scale the analysis. Testing results on real Web3 applications demonstrate the effectiveness of the methodology, particularly in reducing analysis time and improving the accuracy of error detection. An important aspect of the research is the continuous updating of knowledge bases and analysis tools, enabling the consideration of new types of attacks and vulnerabilities. The article also highlights the importance of interoperability between different blockchain networks, which remains a challenging task but is a key element for the future development of Web3. The research results show that the proposed methodology is promising for scaling and adapting to new challenges in blockchain ecosystems such as Solana. Thus, the developed approach to automated smart contract verification not only enhances the security of Web3 applications but also contributes to their further development, ensuring stability and reliability in the dynamic evolution of blockchain technologies.
The paper presents the main approaches to the construction of the PKI public key architecture divided into basic, two-level, and multi-level hierarchies. Modern methods of attacks on existing public key infrastructures, protocols for building secure connections of both wired and wireless systems are considered. The basics of the class of attacks on PKI infrastructures are defined, of which the main attention is paid to the most dangerous class of attacks – man-in-the-middle (MITM-attacks). The paper provides models of various classes of MITM attacks, their details and existing methods of reducing the risks of their implementation. Existing examples of successful attacks on enterprises and various organizations that implemented MITM attack models at the application, network, and physical levels of the network interaction model are also given. For the PKI infrastructure, one of the options is its segmentation, which allows to reduce the scope of attacks on the key certification center. The paper also provides an alternative way to protect against MITM attacks using distributed micro ledger technology (DLT) to create a decentralized cryptographic key distribution system (DKMS). The solution is based on the use of micro ledgers (distributed ledger technology – DMLT). Using DMLT to create a DKMS allows protection against additional classes of MITM attacks.
Relevance: Starting with the invention of the Internet, the world began to change rapidly, and the pace of change is increasing, so the problem of data storage and processing is becoming more and more relevant. The ZK-STARK protocol is a new cryptographic zero-knowledge proof protocol that is not yet widely used in practice and allows you to check a message or a transaction on the blockchain network for authenticity without reproducing it completely. At the moment, gaps and problems related to this protocol are identified: computational complexity, possible poor compatibility with other protocols, and resistance to attacks from quantum computers. Therefore, the paper aims to supplement the coverage of the problem associated with computational complexity and to propose solutions to this problem. Purpose: on the basis of the theoretical implementation of the first stage named Arithmetization of the ZK-STARK protocol, to test its software implementation in order to provide recommendations on its most computationally efficient version. Research methods: mathematical statements on interpolation theory, group theory, number theory; information on Fibonacci numbers; information on the Euler function; generating element of a group; cyclic groups; Lagrange interpolation polynomial and the sequence of calculations of Arithmetization; Visual Studio 2022 programming environment, C++ programming language, NTL library, Microsoft Excel. Results of work: The result of the work is the theoretical implementation of the first stage of the ZK-STARK protocol and the effectiveness testing of the first stage, and providing recommendations for its most effective version. Conclusion: Testing has shown that the practical implementation of the Arithmetization based on the inverse fast Fourier transform has a time complexity , that is in times less than the time complexity of the Arithmetization based on inverse matrices method and Gaussian method for interpolation, that speeds up the work of Arithmetization of the ZK-STARK protocol.
Open access
Cybersecurity and Information Systems
Advanced Computational Techniques in Science and Engineering
The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.
Олег Гарасимчук, Юрій Наконечний, Тарас Луковський, Роман Андріїв · 5 authors
Постійне збільшення обсягів даних породжує проблеми, пов'язані з вибором ефективних методів та засобів зберігання, а також забезпеченням захисту цих даних від несанкціонованого доступу. У статті детально розглянуто критичну тему збереження та захисту важливої інформації в умовах зростання обсягів даних і численності кібератак. Необхідність надійного збереження і захисту даних наростає, особливо у контексті підвищеної загрози з боку зловмисників. Висвітлюється, як блокчейн-технології, особливо на базі платформи Ethereum, можуть вирішити проблеми надійного збереження і безпеки даних. Ethereum пропонує альтернативу традиційній клієнт-серверній моделі, децентралізуючи зберігання даних за допомогою розподіленої мережі вузлів. Ця технологія значно підвищує безпеку, ускладнюючи несанкціонований доступ до інформації, оскільки для злому приватного ключа потрібні значні обчислювальні ресурси. Смарт-контракти на Ethereum дозволяють створювати застосунки, які виконуються точно відповідно до заздалегідь визначених умов, без можливості втручання третіх осіб. Це особливо важливо для месенджерів, де конфіденційність і доступність даних мають принципове значення. Вартість транзакцій в блокчейні, хоча й висока, компенсується високою надійністю та безпекою зберігання даних. Застосована методологія підтверджує, що використання публічного (децентралізованого) сховища даних є безпечним, оскільки зламати приватний ключ Ethereum практично неможливо.
The prevalence of financial fraud poses significant challenges to global financial stability, resulting in billions of dollars in losses annually and undermining consumer trust in financial institutions. With the increasing complexity and volume of financial transactions driven by the rapid growth of digital banking and e-commerce, traditional fraud detection methodologies have proven inadequate in addressing the scale and sophistication of modern fraudulent activities. This paper seeks to investigate and delineate the development of advanced data science and artificial intelligence (AI) methodologies aimed at detecting, mitigating, and preventing financial fraud in real-time systems. By exploring a range of state-of-the-art models, algorithms, and technologies, this research aims to provide comprehensive insights into how these systems can be deployed effectively to safeguard financial operations and maintain systemic integrity. Financial fraud detection is inherently challenging due to the dynamic and evolving nature of fraudulent tactics. The emergence of techniques such as machine learning (ML) and deep learning (DL) has significantly enhanced the ability to identify complex, non-linear patterns within large datasets that were previously undetectable by conventional rule-based systems. This paper focuses on the integration of supervised, unsupervised, and semi-supervised learning methods, as well as hybrid approaches that combine different algorithmic strategies for greater detection accuracy. In the context of financial fraud, algorithms such as decision trees, support vector machines (SVM), random forests, and neural network architectures have been adapted and fine-tuned to operate under stringent latency constraints inherent in real-time processing systems. Moreover, the adaptation of generative adversarial networks (GANs) for synthetic data generation and anomaly detection is examined to bolster the robustness and adaptability of fraud detection models. A critical aspect of this research lies in the exploration of feature engineering and data pre-processing techniques to optimize the input datasets for AI models. Given that the quality of data directly influences the efficacy of predictive algorithms, innovative feature extraction, dimensionality reduction, and data augmentation methods are discussed in detail. The use of time-series analysis and sequence modeling, especially through recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, is emphasized for fraud detection in transactions that require contextual and sequential understanding. Such methodologies enable the capture of temporal dependencies that are essential for detecting anomalous behaviors indicative of fraudulent activities. Additionally, the paper addresses the significance of explainable AI (XAI) in the realm of financial fraud prevention. Trust in AI-driven fraud detection systems can be undermined by their "black-box" nature, where decision-making processes remain opaque to users and regulators. As such, incorporating interpretable models and explainability tools is essential for meeting regulatory requirements and fostering confidence in automated systems. This research evaluates various XAI techniques, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), and their integration with AI models to ensure that the decision-making process can be audited and understood by human analysts. The paper also explores the real-world applicability of AI and data science-based fraud detection through case studies of financial institutions and tech firms that have implemented such systems. These case studies illustrate the challenges faced, such as the need for real-time processing, false positive management, and system scalability. Furthermore, it provides an analysis of the trade-offs between model accuracy, computational resources, and real-time performance requirements. The dynamic nature of fraud tactics demands adaptive learning mechanisms that can update models in response to new data, which brings attention to the necessity of continuous learning and model retraining protocols. Techniques such as online learning and active learning are discussed as viable solutions to ensure that models remain effective against emerging fraud patterns. The challenges of data privacy and security are also examined, given the sensitive nature of financial data. AI and ML models, particularly those deployed in real-time environments, must comply with stringent data protection laws such as the General Data Protection Regulation (GDPR) and regional financial regulations. The implications of privacy-preserving machine learning, differential privacy, and federated learning as methods to process data without compromising individual user privacy are evaluated. This aspect is critical for building trust between financial institutions and customers, ensuring that fraud detection efforts do not come at the expense of user data confidentiality. Lastly, the research covers future directions and emerging trends that could shape the landscape of financial fraud detection and prevention. The integration of blockchain technology and distributed ledger systems is considered for enhancing transparency and reducing opportunities for fraudulent activities. Advanced threat intelligence platforms that leverage cross-industry data sharing and the collective insights of AI models trained on diverse datasets are also discussed as potential avenues for mitigating fraud in a proactive manner. The role of collaborative networks and the potential for AI-driven fraud detection to be part of a larger cybersecurity framework are posited as next-generation solutions to create a more secure financial ecosystem. The findings of this research underline the significance of continuous advancements in data science and AI to stay ahead of increasingly sophisticated financial fraud tactics. While AI models have shown promising capabilities in detecting fraudulent activities in real-time, challenges such as model interpretability, scalability, and adaptability remain prominent. This paper concludes with a strategic roadmap for financial institutions, policymakers, and technology developers to enhance the efficacy of fraud prevention strategies, which include fostering innovation in AI-driven solutions, promoting the development of robust real-time processing infrastructures, and encouraging collaborative research efforts that leverage cross-sector knowledge and resources.