Anak Agung Lingga Pratyaksa Nugraha, Ni Wayan Emmy Rosiana Dewi, Fajar Purnama
The development of the entertainment industry, especially music concerts, has driven the transformation of ticket sales systems from conventional to digital methods. Although online concert ticket sales offer greater convenience and reach, they still face the risks of fraud, counterfeit tickets, and unfair distribution. This study, Blockchain Smart Contract Implementation for NFT-Based Online Music Concert Ticket Transactions, aims to develop a ticket sales system using blockchain technology by integrating smart contracts and Non-Fungible Tokens (NFTs). The main objectives are to design and implement smart contracts on the Ethereum network, implement ERC-721-based digital tickets, ensure transparency in transaction history, and verify ticket authenticity through unique identifiers. This study adopts the Agile method, with implementation on the Ethereum Sepolia Testnet and testing using the meta mask digital wallet. The results show that the developed system can automatically hold funds through an escrow mechanism until the ticket is downloaded, generate unique and tamper-proof NFT tickets, display transaction details transparently, and facilitate ticket verification effectively. In conclusion, the use of smart contracts and NFTs significantly improves the security, transparency, and trustworthiness of online music concert ticket transactions.
Health-care is undergoing a considerable digital shift in the present state, which is driven by the rise of new technologies and the changes taking place globally. The movement is rebalancing the provision and availability of health care, at the same time that it highlights the importance of protecting confidential information about patients. Coupled with the cryptographic primitives, blockchain technology provides a formidable answer, as it promises to improve data integrity using decentralized processes. In this paper, a hybrid blockchain-based EHR management and security solution to Electronic Health Records (EHRs) is described. Having considered the drawbacks of the blockchain in its ability to work with large files the system is connected with Ethereum blockchain through Ganache and program construction tools is equipped with the InterPlanetary File System (IPFS). In the hybrid model, one does store each row hash (unique identifier) of the patients records on the blockchain, but one does not store the actual data on the blockchain, instead on IPFS. A Decentralized Application (DApp) built on the programing language of Ethereum, Solidity, and the web3.js interface also allows secure data access via cryptocurrency wallets like MetaMask. The use of smart contracts is deployed to process transactions to achieve transparency and verifiability. To enhance security the Elliptic Curve Digital Signature Algorithm (ECDSA) is adapted to provide unauthorised access. Results of simulation reveal that a suggested method is reliable in providing patient data security, maintain immutability, and secure exchange of data. The approach promotes transparency within the digital health-care systems and strengthens the stakeholder belief by allowing a decentralised structure of these systems.
Ch Aruna, T Varsha, C Shivakumar, M. U. Sreeja ¡ 5 authors
In modern healthcare systems, the centralized storage of electronic health records (EHRs) and associated documents presents significant vulnerabilities, including single points of failure, data tampering, and privacy breaches.This research proposes a decentralized framework that integrates blockchain technology with the InterPlanetary File System (IPFS) to securely manage doctor-patient appointment data, medical reports, prescriptions, and feedback.The system utilizes a permissioned Ethereum-style smart contract, named "Healthcare," to immutably store user profiles, appointment metadata, prescription references, and patient ratings.Sensitive medical files are encrypted using AES-CTR mode, with encryption keys derived via PBKDF2, and pinned to a local IPFS node to ensure both confidentiality and decentralized availability.Patients can register and log in, browse a list of doctors with on-chain average ratings, and book appointments by uploading encrypted medical reports.Later, they can securely download prescriptions in decrypted form.Doctors access the platform to view pending appointments, retrieve patient reports, issue encrypted prescriptions, and publish them on-chain.Patient feedback is also recorded as immutable blockchain transactions.To improve system responsiveness, an in-memory Python cache is synchronized with the blockchain, enabling fast read operations without repeatedly querying the chain.Experimental results demonstrate that the encryption, IPFS pinning, and decryption processes operate within acceptable performance thresholds.The proposed system enhances data integrity, transparency, and fault tolerance: no single authority can alter records without detection, and encrypted files remain retrievable even if specific nodes fail.By combining decentralized metadata storage with secure, off-chain encrypted file handling, this framework offers a robust, patient-centric solution to modern EHR management challenges.
<p>Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.</p>
With the growing demand for blockchain technology, the deployment of various applications has highlighted the critical chal- lenge of balancing scalability, security, and decentralizationâtermed the blockchain trilemma. To ensure blockchainâs effectiveness in real-world scenarios, resolving this trilemma with minimal trade-offs is crucial. Despite recent advancements, existing solutions, including Filecoinâs decentralized storage, have not fully addressed these challenges. While Filecoin reduces on-chain data re- dundancy through cryptographic proofs (PoRep/PoSt), its throughput remains insufficient (< 50 TPS) and vulnerable to adversarial attacks like selfish mining. In this study, we propose a novel Filecoin-based architecture that tackles the trilemma by reducing trans- action size to 50.6 bytes via Data Identification Numbers (DINs), achieving 145 TPS (a 20 Ă improvement over baseline Filecoin) with 1 MB blocks while maintaining 80% chain quality under 45% adversarial influence. Our approach enhances decentralization by minimizing storage requirements (292 bytes per reference block) and lowering hardware demands for mining nodes ($500/n- ode). Cost-efficiency analysis demonstrates a 99.9% reduction in energy consumption (0.001 kWh/Tx) compared to Bitcoinâs PoW (1,200 kWh/Tx) and a 3,448 Ă improvement in storage efficiency over baseline Filecoin. Security is preserved through PoRep/PoSt optimizations, resisting double-spending and Sybil attacks. Theoretical and empirical evaluations, including adversarial simulations and comparisons with Bitcoin, Ethereum, and Filecoin, validate unprecedented scalability-security-decentralization trade-offs. This work sets a new benchmark for blockchain systems, enabling decentralized applications to rival centralized systems in throughput, cost, and robustness.
William Villegas-Ch, Jaime Govea, Rommel GutiĂŠrrez
This study aims to optimize blockchain consensus mechanisms by integrating artificial intelligence techniques to address critical limitations in latency, scalability, computational efficiency, and security inherent in traditional protocols, such as PoW, PoS, and PBFT. The proposed model combines deep neural networks (DNNs) for feature extraction with deep reinforcement learning (DRL), specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to enable dynamic validator selection and real-time adjustment of consensus difficulty. The training process utilizes a hybrid dataset of historical blockchain records from Ethereum and Hyperledger networks and synthetic data from simulated attack scenarios involving Sybil, 51%, and DoS threats. Experimental evaluations were conducted in private and permitted environments under varying transactional loads. Results show a 60% reduction in confirmation latency compared to PoW, achieving 320 ms, and a 20% improvement over PBFT. Transaction throughput increased to 22,000 transactions per second (TPS), and computational resource consumption was reduced by 30%. The model achieved an attack tolerance of up to 92%, significantly enhancing network resilience. The novelty of this work lies in its autonomous consensus optimization strategy, which enables adaptive and secure protocol behaviour without manual intervention, representing a scalable and efficient solution for future blockchain infrastructures.
This research investigates the effect of social media sentiment on the cryptocurrency market, particularly focusing on Bitcoin and Ethereum. Using TensorFlow as a machine learning tool, we developed a sentiment index from 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum, collected in 2022. The sentiment scores, ranging from -1 (negative) to 1 (positive), were categorized into positive, neutral, and negative classes and analyzed alongside daily return and volatility metrics for both cryptocurrencies using a Vector Autoregression (VAR) model. Our study identifies significant impacts of social media sentiment on cryptocurrency markets. Specifically, Bitcoinâs returns show a heightened sensitivity to negative sentiment, whereas Ethereumâs returns remain unaffected by any sentiment type. However, the volatility of both cryptocurrencies is affected by neutral sentiment. These findings highlight distinct behavioral patterns across cryptocurrencies and uncover a bidirectional relationship between market dynamics and social media sentiment. This study offers novel insights into how public perception influences digital asset markets, thereby contributing to the behavioral finance literature and providing practical implications for investors and policymakers.
Cryptocurrency is a digital innovation in the financial sector that has revolutionized the global transaction system through blockchain technology. One of the main challenges in the crypto domain today is determining the price of cryptocurrencies, which are highly volatile. Ethereum, one of the largest cryptocurrencies, exhibits complex volatility patterns that require a robust predictive system. This study aims to compare the performance of the standard XGBoost algorithm with XGBoost optimized using Bayesian Optimization in predicting daily Ethereum prices based on time series data from 2016 to June 2025. The dataset includes price-related features such as open, high, low, volume, and percentage price change. The modeling process consists of several stages including feature engineering, time series-based data splitting, and model training. Model performance was evaluated using three primary metrics: MAE, RMSE, and R² Score. The evaluation results show that the standard XGBoost model achieved an MAE of 80.8926 (3.12%), RMSE of 114.1457 (4.40%), and an R² Score of 0.9723. Meanwhile, the optimized model using Bayesian Optimization achieved an MAE of 70.7241 (2.73%), RMSE of 102.5334 (3.96%), and an R² Score of 0.9777. These results indicate that Bayesian Optimization helps improve the model's prediction accuracy. This study concludes that the XGBoost model with a Bayesian optimization approach yields superior and more effective performance in forecasting Ethereum prices based on time series data.
In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems.
Blockchain technology has become an essential tool for enhancing reliability and security across several industries, including the healthcare sector. In this work, we propose and implement an Ethereum-based blockchain framework to decentralize electronic health records (EHRs) at Tumakuru Siddaganga Hospital. The system establishes an append-only chain of transaction blocks that guarantees the confidentiality, auditability, and integrity of patient health records. By design, only authorized healthcare professionals can access patient data, and even then, only with the patientâs explicit consentâensuring a privacy-preserving access model. Our approach demonstrated a 40% reduction in data access delays and eliminated unauthorized access attempts through smart contract-based access control. The decentralized nature of the framework reduces reliance on centralized databases, significantly lowering the risk of data tampering and breaches. Additionally, the implemented consensus protocol ensures that only verified transactions are recorded, maintaining consistency across distributed nodes. Compared to traditional systems, our blockchain-based solution improved the traceability of health data access events by 100%, ensuring transparency and accountability. These findings validate that blockchain technology can substantially enhance data sharing, integrity, and patient control in modern healthcare systems.
Cryptocurrency markets are highly volatile, creating challenges for accurate risk management and forecasting. As digital assets become more integrated into financial systems, understanding their volatility dynamics is essential for investors and policymakers. Previous research has primarily applied standard GARCH models to cryptocurrencies, often neglecting advanced specifications that capture asymmetry, regime-switching, and long-memory effects. This limits the accuracy of volatility forecasts and fails to reflect the unique behaviour of digital assets. This study aims to identify the most effective GARCH-class models for forecasting volatility in Bitcoin, Ethereum, Binance Coin, and Ripple. We analyse daily returns from August 2017 to December 2024, applying eight advanced GARCH specifications: EGARCH, GJR-GARCH, FIGARCH, HYGARCH, MSGARCH, CS-GARCH, and Log-GARCH. Hyperparameter tuning is conducted via grid search across lag orders (p, q â [1, 5]), mean equations, and error distributions. Model performance is evaluated using AIC, BIC, RMSE, and MAE. Results show that MSGARCH and EGARCH outperform symmetric and short-memory models, highlighting the importance of regime-switching and leverage effects. FIGARCH provides the best fit for Bitcoin and Ethereum, confirming long-memory persistence. Skewed Studentâs t and GED distributions improve accuracy by capturing heavy tails and asymmetry. These findings demonstrate the limitations of standard GARCH models and underscore the value of advanced specifications in modelling cryptocurrency volatility. The study offers practical insights for traders and risk managers, contributing to more robust forecasting in non-stationary markets. Advanced GARCH models significantly enhance volatility prediction for digital assets. Future research could extend this framework to other speculative instruments or integrate machine learning techniques to further improve performance.
As Ethereum continues to gain traction as a leading blockchain platform, its open and decentralized nature has also made it an attractive target for fraudulent activities. This paper presents a machine learning-based approach to detect fraudulent Ethereum transactions by analyzing behavioral patterns within transaction data. Using a labeled dataset of Ethereum transactions, various classification algorithms such as Random Forest, XGBoost, and Support Vector Machines were trained and evaluated. The proposed system focuses on identifying anomalies and suspicious transaction behavior by extracting relevant features like gas usage, transaction value, and timing. Experimental results show that the model can achieve high accuracy and precision in distinguishing between legitimate and fraudulent transactions. This work contributes to the growing field of blockchain security by demonstrating the viability of intelligent fraud detection techniques and providing a framework that can be integrated into real-world applications.
Irum Matloob, Shoab Ahmed Khan, Bushra Bashir, Rukaiya Rukaiya ¡ 6 authors
Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors' and hospitals' behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.
Smart contracts and cryptocurrency wallets are foundational components of decentralized applications (dApps) on blockchain platforms such as Ethereum. While these technologies enable secure, transparent, and automated transactions, their integration also introduces complex security challenges. This study presents a security-oriented analysis of smart contract and wallet integration, focusing on BlockScribeâa decentralized Ethereum-based application for digital record certification. We systematically identify and categorize security risks arising from the interaction between wallet interfaces and smart contract logic. In particular, we analyze how user authorization flows, transaction design, and contract modularity affect the security posture of the entire dApp. To support our findings, we conduct an empirical evaluation using static analysis tools and formal verification methods, examining both contract-level vulnerabilities and integration-level flaws. Our results highlight several overlooked attack surfaces in walletâcontract communication patterns, including reentrancy amplification, permission mismanagement, and transaction ordering issues. We further discuss implications for secure dApp development and propose mitigation strategies that improve the robustness of walletâcontract ecosystems. This case study contributes to a deeper understanding of integration-layer vulnerabilities in blockchain-based systems and offers practical guidance for developers and auditors aiming to strengthen smart contract security.
AndrĂŠ Davi Lopes, Tais Mello, Wesley dos Reis Bezerra
This paper presents the development of a distributed digital identity system utilizing modern technologies, including FastAPI, MongoDB, gRPC, Docker, and blockchain simulation with Ganache and Ethereum. The objective is to demonstrate the benefits of distributed systems and blockchain for the security, traceability, and decentralization of digital identities. The methodology included the development of a microservices architecture with JWT authentication, data persistence in MongoDB, simulation of blockchain operations using Ganache, and containerization with Docker. The results demonstrate the feasibility of the proposed approach, with a functional web interface, complete audit logs, and blockchain simulation with Ethereum. The theoretical foundations, technical implementation, results obtained, and prospects for integration with real blockchain networks are discussed.
Carlos Beis-Penedo, Francisco TroncosoâPastoriza, Rebeca P. DÄąĚaz Redondo, Ana FernĂĄndez Vilas ¡ 6 authors
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.
The global food waste crisis poses significant environmental and economic challenges, particularly within institutional settings such as university campuses. At NSBM Green University, South Asiaâs first green university, traditional food waste management systems are hampered by opacity and unverifiable data, impeding effective reduction strategies. This paper proposes a blockchain-based Smart Campus Waste Management Platform to revolutionize food waste tracking in campus canteens. Utilizing blockchainâs immutability, smart contracts, and integrated authentication, the system ensures secure, tamper-proof data management. It captures real-time waste data through authenticated manual inputs, processes it via a secure gateway, and records it on an Ethereum ledger, enabling precise sustainability reporting and data-driven interventions. The study details the systemâs architecture, implementation with Ethereum and Solidity, and alignment with NSBMâs sustainability goals. Key benefits include enhanced accountability, optimized waste reduction, and reinforced green leadership, with future enhancements such as AI integration, scalability, and incentive mechanisms addressing potential challenges.
Engin Zeydan, Josep ManguesâBafalluy, Ĺuayb S. Arslan, Yekta TĂźrk ¡ 5 authors
The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based networkâsharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchainâbased network sharing solutions and outlines future research directions.
Ethereum, a leading blockchain platform, has revolutionized the digital economy by enabling decentralized transactions and the execution of smart contracts. Ethereum transactions form the backbone of its network, facilitating peer-to-peer exchanges and interactions with complex decentralized applications. Smart contracts extend Ethereum's capabilities by automating processes and enabling trustless execution of agreements. Hence, understanding how these smart contracts interact is important in order to facilitate various performance optimizations, such as warming objects before they are being accessed and enabling concurrent execution. Of particular interest to us are the development of the calling graph, as well as the read sets and write sets of invocations within the same block, and the properties of the associated conflict graph that is derived from them. The latter is important for understanding the parallelization potential of smart contracts on Ethereum. We traced upwards of 2 million recent Ethereum blocks using call tracer and prestate tracer, out of a total of 21.4 million blocks at the time of writing. We report on the transactions per block distribution, the structure of call trees in smart contract invocations, the ratio of value-transfer transactions to smart contract invocations, as well as provide a comprehensive study of the structure of blocks' conflict graphs. We find that conflict graphs predominantly show a star like configuration, as well as other noteworthy structural properties.
Hadis Rezaei, Mojtaba Eshghie, Karl Anderesson, Francesco Palmieri
While catastrophic attacks on Ethereum persist, vulnerability research remains fixated on implementation-level smart contract bugs, creating a gap between academic understanding of vulnerabilities and the root causes of high-impact, real-world incidents. To address this, we employ a two-pronged methodology: first, a systematic literature review of 71 academic papers to build a catalog of 24 active and 5 deprecated vulnerabilities. Second, we conduct an in-depth, empirical analysis of 50 of the most severe real-world attacks between 2022 and 2025, collectively incurring over $1.09B in losses, to identify their root causes. We introduce the concept of "exploit chains" by revealing that many incidents are not caused by isolated vulnerabilities but by combinations of human, operational, and economic design flaws that link with implementation bugs to enable an attack. Our analysis yields insights on how decentralized applications are exploited in practice, leading to a novel, four-tier root-cause framework that moves beyond code-level vulnerabilities. We find that real-world successful attacks on Ethereum (and related networks) trace back to one of the four tiers of (1) protocol logic design, (2) lifecycle and governance, (3) external dependencies, and (4) classic smart contract vulnerabilities. We investigate the suitability of this multi-tier incident root-cause framework via a case study.
The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the systemâs ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products.
Fintech provides technological services to increase operational efficiency in financial institutions, but traditional perimeter-based defense mechanisms are insufficient against evolving cyber threats like insider attacks, malware intrusions, and Advanced Persistent Threats (APTs). These vulnerabilities expose Fintech organizations to significant risks, including financial losses and data breaches. To address these challenges, this paper proposes a blockchain-integrated Zero Trust framework, adhering to the principle of "Never Trust, Always Verify." The framework uses Ethereum smart contracts to enforce Multi Factor Authentication (MFA), Role-Based Access Control (RBAC), and Just-In-Time (JIT) access privileges, effectively mitigating credential theft and insider threats, the effect of malware and APT attacks. The proposed solution transforms blockchain into a Policy Engine (PE) and Policy Enforcement Point (PEP), and policy storage, ensuring immutable access control and micro-segmentation. A decentralized application (DApp) prototype was developed and tested using STRIDE threat modeling, demonstrating resilience against spoofing, tampering, and privilege escalation. Comparative analysis with Perimeter-based systems revealed a trade-off: while the framework introduced a marginal latency increase (74.0 ms vs. 49.33 ms) and reduced throughput (30.77 vs. 50.0 requests/sec), it significantly enhanced security by eliminating single points of failure and enabling tamper-proof audit trails. Experimental validation on a 200-node simulated network confirmed the framework's robustness, with future optimizations targeting Layer-2 solutions for scalability. This work bridges the gap between Zero Trust theory and practical blockchain implementation, offering Fintech organizations a decentralized, cost-effective security model.
This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016â2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.
Yul is an intermediate language used in the compilation of the Solidity programming language for Ethereum smart contracts. The compiler applies customizable sequences of transformations to Yul code. To help ensure the correctness of these transformations and their sequencing, we used the ACL2 theorem prover to develop a formalization of the syntax and semantics of Yul, proofs relating static and dynamic semantics, a formalization of some Yul code transformations, and correctness proofs for these transformations.