The protection of sensitive medical information has become a critical concern in modern digital healthcare. This study introduces a Hybrid Architecture that ensures secure and reliable healthcare data management through the integration of blockchain technology with off-chain and on-chain mechanisms. Patient records are encrypted using AES-256-GCM, stored in the InterPlanetary File System (IPFS), and verified using Merkle Tree structures, with only the root values anchored on Ethereum smart contracts. This design guarantees data security and integrity while achieving significant gas optimization by reducing on-chain storage costs. Experimental evaluation demonstrates that the proposed system achieves high scalability, efficient transaction processing, and strong resistance to tampering, ensuring confidentiality and auditability. By combining blockchain, cryptographic techniques, and distributed storage, the framework addresses pressing challenges of security, privacy, and trust in healthcare ecosystems. The results highlight the potential of Hybrid Architecture models to deliver a cost-effective, privacy-preserving, and scalable solution for next-generation Healthcare Data Security.
Traditional financial services are characterized by high complexity, multi-level structures, and dependence on intermediaries, which create significant operational costs, lengthy settlement times, and system risks. The article analyzes modern financial systems such as SWIFT, clearinghouses, central securities depositories, and central counter-parties, highlighting the key drawbacks of a centralized approach: data fragmentation, the need for synchronization, failure risks, and high compliance costs. The article also addresses contemporary challenges, including slow settlement times (for example, T+2 for equities), reliance on correspondent banking networks, and costs associated with risk management. The aim of the article is to survey the impact of blockchain technologies on the financial sector, assessing the opportunities and challenges of implementing blockchain to optimize financial processes. The article compares traditional financial systems with innovative approaches based on distributed ledgers, such as blockchain, in terms of performance, transparency, security, regulatory compliance, and cost. The research methodology includes a review of the relevant literature, an overview of existing platforms (Ethereum, Hyperledger Fabric, R3 Corda, Quorum), and their applications in the financial sector. The article examines the technological aspects of blockchain, including distributed ledgers, consensus algorithms, smart contracts, and asset tokenization. The advantages of blockchain technology are identified, particularly the automation of processes, reduction of reliance on intermediaries, increased transparency, and shortened settlement times. The prospects of decentralized finance (DeFi) and corporate blockchain solutions are analyzed, particularly the use of smart contracts and tokenization to enhance liquidity. The research results indicate that the implementation of blockchain can significantly reduce operational costs, enhance transaction transparency, and ensure the speed of financial settlements. In particular, blockchain shortens the T+2 settlement cycle to seconds, improving liquidity and efficiency in financial markets. The conclusion of the article offers recommendations for selecting blockchain platforms based on the needs of financial institutions. Key criteria such as confidentiality, scalability, transaction throughput, and compliance with regulatory requirements are discussed. The necessity of creating a unified regulatory framework to support the implementation of blockchain in finance is particularly emphasized. Future research prospects include the development of interoperability tools, enhancing the security of smart contracts, and long-term evaluation of the efficiency of blockchain solutions in production environments. Thus, blockchain is an important tool for the transformation of financial systems, ensuring a significant increase in their efficiency, resilience, and transparency.
Open access
Economic and Technological Developments in Russia
Economic, Social, and Public Health Issues in Russia and Globally
Alanoud M. Almhlbdi, Norah D. Altowairqi, Areej Alshutayri, Rehab Qarout
Identifying hidden payloads in images has become increasingly critical as steganography continues to challenge traditional security measures. This paper introduces a deep learning framework for both the detection (binary classification) and fine-grained classification (multi-class) of steganographic payloads embedded using Least Significant Bit (LSB) techniques. The proposed system distinguishes between benign images and stego images containing five different payload types: HTML, JavaScript, PowerShell, URLs, and Ethereum-related data. To achieve this, we systematically evaluate various architectures, including a custom Convolutional Neural Network (CNN), hybrid CNN-GRU and CNN-LSTM models, and a Vision Transformer (ViT) at different input resolutions using 5-fold cross-validation. Our experiments reveal a critical finding: image resizing significantly degrades detection performance, as subtle LSB artifacts are often corrupted. While our custom CNN model achieved the highest mean cross-validation accuracy (0.9702), the hybrid CNN-GRU model demonstrated superior generalization on the held-out test set and external dataset, achieving a multi-class accuracy of 0.98 on the testset and 0.97 on the external. This result highlights the advantage of combining the CNN’s spatial feature extraction with the GRU’s ability to model sequential dependencies for robust payload identification on unseen data.
Open access
Advanced Steganography and Watermarking Techniques
Rashid Ul Haq, Rahim Khan, Fahad Alturise, Shafrida Sahrani · 6 authors
Recent technological advances have enabled researchers to investigate various novel approaches utilized to manage allograft transplants and overcome the challenges of conventional centralized systems. The rising need for transparency, efficiency, and, especially, security in this highly sensitive medical procedure necessitates the use of decentralized solutions like blockchain rather than existing centralized approaches. However, the current state of research is theoretical and unproven, and allograft management lacks any reliable, cost-effective, or data-proven solution. In this paper, we propose an Ethereum blockchain-based allograft transplantation management system that can address all of those issues linked to the existing solutions. The proposed approach aims to enhance traceability, transparency, and data provenance across the entire allograft transplant process. We present six reliable and cost-efficient algorithms, as well as a comprehensive system architecture, to provide valuable insight into system implementation complexity. We have designed an efficient smart contract implementing the proposed algorithms to ensure flawless execution of allograft donation, transportation, and transplantation. We conduct thorough tests, validation, security, cost, throughput, and latency assessments of the system in order to contrast its effectiveness with existing solutions and results shows that our solution is cost-effective, as well as secure and efficient. We generalized the proposed solution so that, with minimal changes, it could be used for other problems and addressed some of the technical and ethical challenges.
In developing nations like India, the agri-food supply chain faces challenges and difficulties such as minimal traceability, limited transparency, and reliance on intermediaries, which can lead to farmer abuse and eroded customer trust. This study suggests and evaluates a paradigm that combines blockchain technology with artificial intelligence (AI) to improve agri-food systems' traceability, transparency, and equity. IoT-based sensing, IPFS for decentralised data storage, Ethereum-based smart contracts, and AI-driven analytics with a Random Forest model are all integrated into the system. Using a hypothetical onion supply chain case study, a four-layer architecture comprising user interface, AI analytics, blockchain infrastructure, and IoT data capture was created and assessed. Major performance gains over conventional systems are demonstrated by the results, which include a 35% decrease in operating expenses, 90% data consistency, and 75% increased transparency. A 93% task completion rate and a System Usability Score (SUS) of 78.2 were obtained from stakeholder usability testing. Proof-of-authority consensus has been suggested as a remedy for Sybil attacks and consensus delays, which are the issues in low-connectivity areas. A suitable roadmap for implementing blockchain-AI solutions in agriculture is presented in this paper. Multilingual interfaces, offline functionality, voice assistance, federated learning for privacy-preserving AI, and real-world scaling through platforms like Polygon or Hyperledger will be the main areas of future growth.
Urooj Waheed, Muhammad Ahsan Khan, Yusra Mansoor, Huma Jamshed · 5 authors
In any democratic electoral system the fundamental right of every eligible citizen is to vote in order to elect the desired representative. However minimal efforts have been made to improve the voting mechanism. Many states are still utilizing paper based balloting systems, however technological advancements have led to the introduction of electronic voting machines (EVMs) to improve security and maintain public trust in the electoral process. Irrespective of these efforts, lack of transparency, low voter turnout, and susceptibility to vote rigging persist in these E-Voting systems. Blockchain technology is emerging as a solution to address these challenges by enhancing security and trust in E-Voting, effectively mitigating its longstanding issues. The transparency, immutability, and resistance to tampering provided by blockchain enhance the reliability and credibility of the voting process. This paper presents a decentralized E-Voting system that uses a public blockchain built on Ethereum smart contracts to ensure public accountability and transparency in the E-Voting process.
Yahaya Saidu, Shuhaida Mohamed Shuhidan, Izzatdin Abdul Aziz, Md. Mahmudul Alam · 7 authors
The integration of Blockchain (BC) and the Internet of Things (IoT) has emerged as a transformative solution for addressing traceability challenges in logistics, offering enhanced transparency, data security, and operational efficiency. This study presents a systematic review of the current state of BC-IoT integration for logistics traceability, focusing on its motivations, deployment strategies, technical implementations, and evaluation approaches. A total of 1,619 records were initially retrieved from IEEE Xplore, MDPI, ScienceDirect, Scopus, and Web of Science, from which 61 peer-reviewed studies published between 2015 and 2024 were selected and analyzed. The selection process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous, transparent, and high-quality synthesis. Only studies explicitly addressing the convergence of BC and IoT within logistics traceability contexts were included. Key findings reveal that BC-IoT systems significantly enhance traceability and transparency across logistics networks but face persistent challenges such as scalability, latency, energy efficiency, and security. The review categorizes various deployment architectures including cloud, edge, fog, and hybrid models, and examines their implications for data responsiveness and system reliability. Additionally, it evaluates popular BC platforms (e.g., Hyperledger Fabric, Ethereum, Solana) and consensus mechanisms (e.g., RAFT, PBFT, PoS) based on their suitability for logistics applications. Emerging research directions emphasize the need for cross-chain interoperability, domain-specific frameworks, and decentralized traceability models, particularly in sectors such as humanitarian logistics and regulatory compliance. This review consolidates fragmented knowledge and provides actionable insights for developing scalable, secure, and transparent BC-IoT systems to support next-generation logistics operations.
Iori Suzuki, Yin Minn Pa Pa, Anh Thi Van Nguyen, Katsunari Yoshioka
Decentralized Finance (DeFi) token scams have become one of the most prevalent forms of fraud in Web-3 technology, generating approximately $241.6 million in illicit revenue in 2023 [1].Detecting these scams requires analyzing both on-chain data, such as transaction records on the blockchain, and off-chain data, such as websites related to the DeFi token project and associated social media accounts.Relying solely on one type of data may fail to capture the full context of fraudulent activities.While on-chain data is publicly accessible due to the transparency inherent in blockchain technology, off-chain data often disappears alongside DeFi scam campaigns, making it difficult for the security community to study these scams.To address this challenge, we propose a dataset comprising more than 550 thousand archived web and social media data as offchain data, in addition to on-chain data related to 32,144 DeFi tokens deployed on Ethereum blockchain from September 24, 2024 to January 14, 2025.This dataset aims to support the security community in studying and detecting DeFi token scams.To illustrate its utility, our case studies demonstrated the potential of the dataset in identifying patterns and behaviors associated with scam tokens.These findings highlight the dataset's capability to provide insights into fraudulent activities and support further research in developing effective detection mechanisms.
The global Ayurvedic medicine and herbs industry is projected to reach approximately $23 billion by 2028 (Research, 2022). Ayurveda, a 5000-year-old system of traditional medicine, relies on natural ingredients sourced from diverse ecosystems. However, the Ayurvedic supply chain faces numerous challenges, including raw material authenticity, regulatory compliance, procurement inefficiencies, and counterfeit risks. This research explores the integration of Artificial Intelligence (AI) and blockchain technologies in modernizing Ayurvedic procurement. AI-driven demand forecasting models (LSTM, ARIMA, XGBoost) optimize inventory management, while machine learning-based supplier risk assessment (Gradient Boosting, NLP, Random Forest) enhances vendor selection and fraud detection. Blockchain smart contracts (Hyperledger Fabric, Ethereum) ensure end-to-end traceability, preventing counterfeiting and ensuring compliance with AYUSH, WHO-GMP, and FDA regulations. Additionally, IoT-enabled storage monitoring and hyperspectral AI-based quality authentication maintain herbal potency and safety. The proposed AI-powered procurement framework demonstrates significant improvements in procurement lead time, cost reduction, supply chain transparency, and quality control compared to traditional Ayurvedic sourcing methods. This paper highlights AI’s transformative role in optimizing Ayurvedic procurement, ensuring sustainability, efficiency, and authenticity in global herbal medicine markets.
Eclipse attack is a major threat to the blockchain network layer, wherein an attacker isolates a target node by monopolizing all its connections, cutting it off from the rest of the network.Despite the attack's demonstrated effectiveness in Bitcoin (Usenix'15, SP'20, Usenix'21, CCS'21, SP'23) and partially in Ethereum (NDSS'23, SP'23), its applicability to a wider range of blockchain systems remains uncertain.In this paper, we investigate eclipse attacks against Monero, a blockchain system known for its strong anonymity and pioneering the use of Dandelion++ (the state-of-the-art blockchain network layer protocol for transaction privacy protection).Our analysis of Monero's connection management mechanism reveals that existing eclipse attacks are surprisingly ineffective against Monero.We accordingly introduce the first practical eclipse attack against Monero by proposing a connection reset approach, which forces the target node to drop all benign connections and reconnect with malicious nodes.Specifically, we outline two methods for executing such an attack.The first one exploits the private transaction mechanisms, while the second method leverages the differences in propagation between stem transactions and fluff transactions under Dandelion++.Our attack is not only applicable to Monero but to all blockchain systems utilizing Dandelion++ and similar connection management strategies.We conduct experiments on the Monero mainnet.Evaluation results confirm the feasibility of our attack.Unlike existing eclipse attacks, our connection reset-based approach does not require restarting the target node, significantly accelerating the attack process and making it more controllable.We also provide countermeasures to mitigate the proposed eclipse attack while minimizing the impact on Monero.In addition, we have ethically reported our investigation to Monero official team.
S Remya, Manu J. Pillai, Preethi Ann Jacob, Sruthi Suresh · 5 authors
Certificateless Proxy Re-Encryption (CL-PRE) eliminates certificate management and private key exposure risks for blockchain data sharing, but existing schemes have critical security vulnerabilities and performance limitations. This research work presents comprehensive security analysis and performance evaluation of CL-PRE schemes for blockchain applications. The primary contribution is discovering a critical public key replacement attack against Wang et al.’s CL-PRE scheme, where Type I adversaries completely compromise message confidentiality by substituting legitimate public keys with adversary-controlled keys, enabling ciphertext decryption without private keys and violating IND-CCA security. The systematic performance evaluation of pairing-free PRE schemes for blockchain environments is conducted through extensive benchmarking of three schemes implemented in Go. Results show self PRE achieves superior security but incurs 13.7% higher execution time than certificateless schemes. To address vulnerabilities, this work proposes a secure CL-PRE framework with enhanced validation mechanisms. The Ethereum implementation reduces on-chain storage by 40% while maintaining provable security. The framework achieves 14.1% better performance than existing secure schemes and reduces gas costs by 14.3%. These findings establish security benchmarks and practical guidelines for blockchain developers, emphasizing rigorous cryptographic analysis importance for decentralized access control advancement.
The increasing number of blockchain projects introduced annually has led to a pressing need for secure and efficient interoperability solutions. Currently, the lack of such solutions forces end-users to rely on centralized intermediaries, contradicting the core principle of decentralization and trust minimization in blockchain technology. We propose a decentralized and efficient interoperability solution (aka Bridge Protocol) that operates without additional trust assumptions, relying solely on the Byzantine Fault Tolerance (BFT) properties of the two chains being connected. In particular, relayers (actors that exchange messages between networks) are permissionless and decentralized, hence eliminating any single point of failure. We introduce Random Sampling, a novel technique for on-chain light clients to efficiently follow the history of PoS blockchains by reducing the signature verifications required. Here, the randomness is drawn on-chain, for example, using Ethereum’s RANDAO. We analyze the security of the bridge from a crypto- economic perspective and provide a framework to derive the security parameters. This includes handling subtle concurrency issues and randomness bias in strawman designs. While the protocol is applicable to various PoS chains, we demonstrate the protocol’s practical feasibility by showcasing an instantiated bridge between Polkadot and Ethereum (currently deployed), and discuss some practical security challenges. Furthermore, we evaluate the efficiency of our on-chain light client verifier (implemented as an Ethereum smart contract) against SNARK-based approaches, demonstrating significantly lower gas costs for signature verification - even for validator sets up to 10⁶.
We apply network science methodologies to address analytical challenges in blockchain and Decentralized Finance (DeFi). The pseudonymous nature of Bitcoin and the complex, multi-token interactions of Ethereum-based protocols require tools that go beyond traditional blockchain analysis. We present three network-based frameworks for understanding actor behavior and financial activities in these decentralized systems. First, for Bitcoin, we introduce a money flow representation learning approach that encodes taint networks into graph embeddings to identify entities across multiple address clusters. Second, we analyze DeFi activity using ego network motif mining, which extracts recurring structures from token transfer networks. This method can infer transaction methods (e.g., deposits, swaps, borrowing) and characterizes user behavior, even when labels are incomplete or noisy. Third, we model multi-token interactions through a Multilayer Token Network that links cross-token flows. Using PageRank-CheiRank Trade Balance, we quantify accumulation versus dispersion strategies and uncover temporal shifts in trading behavior, illustrated through entities such as Alameda Research. Together, these frameworks show how network topology, motifs, and multilayer flows transform raw blockchain data into interpretable insights on identity, function, and financial strategy.
This paper presents a comprehensive Model-Driven Engineering (MDE) methodology for automatically transforming Business Process Model and Notation (BPMN) diagrams into executable blockchain-based smart contracts. The proposed approach defines a set of Atlas Transformation Language (ATL) rules that systematically map BPMN elements to Solidity con-structs, ensuring semantic consistency and traceability through-out the transformation process. The framework integrates several stages, including process modeling, model validation, code generation, and deployment, supported by tools such as Camunda, Eclipse ATL, Remix IDE, and MetaMask. Experimental vali-dation on the Ethereum Sepolia test network demonstrates the approach’s ability to enhance automation, reduce manual coding errors, and improve synchronization between business work-flows and their on-chain implementations. Compared to existing BPMN-to-blockchain frameworks, the proposed solution offers a unified and reusable transformation pipeline that bridges the gap between business process modeling and blockchain execution. The study concludes that MDE provides a scalable, traceable, and standardized foundation for developing decentralized business process applications.
Blockchain technology has emerged as a transformative innovation, redefining industries through its decentralized and secure framework. Smart contracts—self-executing code deployed on blockchain platforms like Ethereum—enable decentralized applications (dApps) to automate processes across finance, healthcare, and supply chain management. However, their programmability introduces significant security risks, making them susceptible to vulnerabilities that can be exploited by malicious actors. While different detection methods have been developed to address these security concerns, they often remain inadequate. Traditional approaches to smart contract vulnerability detection, such as static and dynamic analysis, are limited by their reliance on predefined rules, making them ineffective for addressing complex, domain-specific vulnerabilities in rapidly evolving decentralized ecosystems. This thesis addresses these challenges by leveraging Large Language Models (LLMs), which have demonstrated exceptional capabilities in contextual understanding and reasoning. Through parameter-efficient fine-tuning techniques, including Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), the research enhances the scalability and accessibility of LLMs for vulnerability detection. The study also examines Retrieval-Augmented Generation (RAG) frameworks to dynamically retrieve and process relevant information. The research develops and evaluates two distinct approaches: fine-tuning LLMs and RAG. The CodeGemma 7B model achieved exceptional results, attaining 94.78% accuracy on the DeFi Hacks & Top200 dataset and 92.52% on the TrustLLM dataset, surpassing previous benchmarks using larger and proprietary models. The best-performing RAG model, Gemma 2, achieved 79.1% accuracy, demonstrating the effectiveness of retrieval-based augmentation. These contributions lay the groundwork for more scalable, efficient, and democratized tools for securing blockchain ecosystems, addressing the limitations of traditional methods while offering cost-effective solutions for safer decentralized systems.
In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.
The field of Industrial Internet of Things (IIoT) is now being duly recognized as a revolutionary area in industrial automation. With the budding interest of knowledge discovery that helps the businesses in applying technology on a large scale, researchers are now faced with several major security concerns. Blockchain integration is one of the suggested applications of technology that could help secure the data during transmission, storage, and knowledge discovery. Moreover, by integrating smart contracts, a secure architecture could also assimilate accountability during data exchange. Thus, we propose a four-layer security architecture that isolates IIoT devices from user-oriented layers and maintains a record of all IIoT devices registered in the organization to prevent malicious devices from corrupting the database and discovery process. Further, by choosing proof of authority (PoA), we ensure the fair functioning of the Blockchain nodes. Through proxy re-encryption for clients, PoA consensus for Blockchain nodes, and Whitelist-based access control for IIoT Devices, we ensure the legitimacy of all participating nodes. We also implement a prototype using a private Ethereum network with proof of authority consensus and present the time taken for the entire exchange and the gas (ethereum currency) consumed per exchange by the contracts. Also we implemented the secure knowledge discovery to understand the significance of the developed scheme. The results show that the exchange can be implemented in an IIoT environment and work with a reasonable amount of resource consumption.