Enforcing a delay between deposits and withdrawals within decentralized finance protocols may make them more secure but less composable. A delay makes flash loan attacks more expensive, but restricts interactions between protocols. In this work, we analyse public blockchain data to determine if this concern is warranted in practice. We measure the duration between corresponding direct deposit and withdrawal function calls across several decentralized finance protocols on Ethereum. We show that direct callers of DeFi protocols typically leave assets locked in these protocols for many blocks, meaning that artificial withdrawal delays are not likely to have a negative impact on user experience.
The purpose of this article is to explore the key aspects of cryptocurrency exchange systems, including their role in storage, exchange, and token staking. By examining the characteristics and features of these exchanges, cryptocurrency users can make informed decisions about how to allocate and store their funds effectively.There are two main types of cryptocurrency exchanges: centralized exchanges (CEX) and decentralized exchanges (DEX). Centralized exchanges are governed by a central authority that manages user funds, providing a more streamlined and user-friendly experience. However, this centralization creates security risks, as users must trust the exchange with their assets. If the platform is compromised or experiences technical failures, users may suffer significant losses. Moreover, centralized exchanges often require identity verification and other regulatory procedures, which can be a barrier for those who prioritize privacy or anonymity in their transactions. On the other hand, decentralized exchanges (DEXs) operate without a central governing body, allowing users to retain control over their funds and trade directly with each other using smart contracts on a blockchain. This decentralization reduces reliance on intermediaries and enhances privacy, but it also presents challenges. DEXs tend to be more complex to use and may require greater technical expertise.Future research should examine how various groups – ranging from individual investors to large financial institutions – are incorporating cryptocurrency exchanges into their financial strategies.
Satellite communication systems (SatCom) is a brand-new network that uses artificial Earth satellites as relay stations to provide communication services such as broadband Internet access to various users on land, sea, air and in space. It features wide coverage, relatively high transmission rates and strong anti-interference capabilities. Security authentication is of crucial significance for the stable operation and widespread application of satellite communication systems. It can effectively prevent unauthorized access, ensuring that only users and devices that pass security authentication can access the satellite network. It also ensures the confidentiality, integrity, and availability of data during transmission and storage, preventing data from being stolen, tampered with, or damaged. By means of literature research and comparative analysis, this paper carries out on a comprehensive survey towards the security authentication methods used by SatCom. This paper first summarizes the existing SatCom authentication methods as five categories, namely, those based on cryptography, Blockchain, satellite orbital information, the AKA protocol and physical hardware respectively. Subsequently, a comprehensive comparative analysis is carried out on the above-mentioned five categories of security authentication methods from four dimensions, i.e., security, implementation difficulty and cost, applicable scenarios and real-time performance, and the final comparison results are following obtained. Finally, prospects are made for several important future research directions of security authentication methods for SatCom, laying a well foundation for further carrying on the related research works.
The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community.In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data.Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis.Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results.Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts.The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two.By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.
Market sentiment refers to the overall feeling of investors and traders have about the state of the market or the price action of a particular asset. The descriptive study focused on the impact of market sentiment on cryptocurrency investment. Specifically, this study answered the following questions using the data collected in an online survey with 2014 respondents: (1) What is the current status of the market sentiment on cryptocurrency investment? (2) What are the common problems encountered by investors in the cryptocurrency market? (3) Is there a significant relationship between the current status of market sentiment on cryptocurrency investment and common problems encountered by investors? And; (4) What countermeasures can be proposed to the impact of market sentiment on cryptocurrency investment? Most of the respondents tended agreed about the current status of the market sentiment on cryptocurrency investment. Their responses tended to generally reflect their optimistic or "bullish" sentiment toward the market, that cryptocurrency holders are knowledgeable about the benefits of positive market outlook, and the market accurately predicted the volatility of cryptocurrency. The respondents were aware of the problems already reported in the literature, including cryptocurrency investment has become an avenue for illegal operations, the emergence of crypto scams, and the complexities of investing in cryptocurrencies. There was no significant correlation between market sentiment and problem encountered by investors. The respondents proposed some countermeasures to ameliorate some of the problems and challenges. The conclusions were confounded by Simpson's paradox. Further research is research to determine if the relationships between the current status of market sentiment on cryptocurrency investment vs. the common problems encountered by investors vary with respect to different mutually exclusive groups of investors.
By combining blockchain technology, machine learning, and artificial intelligence (AI), the banking sector has witnessed a revolution in credit risk reduction in recent years. With an emphasis on predictive analytics and decentralized frameworks, this paper explores the real-world applications of these technologies in the discovery, evaluation, and management of credit risk. The study demonstrates how machine learning models, blockchain's transparent and unchangeable ledger systems, and AI-powered algorithms have greatly increased the precision and effectiveness of credit risk assessments through thorough literature analysis and case studies. The report also examines how financial institutions implement these technologies to improve operational risk management, lower fraud, and create more accurate credit scoring systems. Notwithstanding their promise, there are still significant obstacles to overcome, including data privacy, regulatory compliance, and implementation costs. In order to effectively utilize the advantages of AI, blockchain, and machine learning in reducing credit risk, the article ends with ideas for overcoming these obstacles. Keywords: Artificial Intelligence; Blockchain; Machine Learning; Credit Risk Mitigation; Predictive Analytics; Financial Technology; Credit Scoring; Risk Management; Decentralized Finance; Operational Risk
Blockchain technology is gaining traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in health data management. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain's strong immutability by storing data directly on-chain. In this work, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, highlighting the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, by designing and implementing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves 2x faster data insertion, 500x faster retrieval, 60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data.
Smart Contracts are the central piece of Ethereum and other compatible blockchains.Their role is to build trusted functionality that unknown parties can interact with.However, their value proposition can be undermined by different security exploits.In many cases, vulnerabilities are overlooked not due to neglect but due to a systematic approach in the review process.This paper aims to appeal to existing frameworks for understanding the business context and provide standardized thinking on auditing smart contracts.The power of a framework lies in the fact that it ensures that auditors do not overlook critical aspects of their vulnerability.
Aktham Maghyereh, Mohammad Al‐Shboul, Basel Awartani
Research background: This paper explores the hedging and safe-haven properties of gold-backed cryptocurrencies within the context of conventional cryptocurrencies such as Bitcoin, Ethereum, Tether, and Binance. With the rise of blockchain technology, cryptocurrencies have gained recognition as alternative investment assets, drawing comparisons to traditional safe-haven assets like gold. However, the risk management potential of crypto gold, especially during periods of extreme market volatility, remains under-examined. Purpose of the article: The purpose of this article is to assess the effectiveness of gold-backed cryptocurrencies as hedging instruments and safe havens for investors in conventional cryptocurrencies. By analyzing their tail dependence during extreme market fluctuations, the study aims to determine their risk management utility. Methods: To achieve this, we employ a Student’s t copula structure integrated with an ARMA-GJR-GARCH model to measure the time-varying tail dependence between gold-backed and conventional cryptocurrencies. This approach allows for a comprehensive analysis of both normal and extreme market conditions. We use the Digix Gold Token (DGX) as a representative of gold-backed cryptocurrencies. The study examines four major conventional cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance (BNB) — by analyzing daily closing prices from May 14, 2018, to January 31, 2023, which comprise 1702 observations. The dataset, sourced from coincodex.com, includes periods of significant market stress, such as the COVID-19 pandemic and the Russian-Ukrainian conflict. Findings & value added: The findings reveal a weak association between gold-backed cryptocurrencies and conventional cryptocurrencies, resulting in medium-to-low hedging effectiveness during the sample period. Nevertheless, during crisis periods, a negative association is observed, indicating that gold-backed cryptocurrencies act as effective safe havens in times of market distress. The study contributes to the literature by providing empirical evidence on the risk management benefits of crypto gold, particularly during financial crises, and highlights its potential inclusion in portfolios with cryptocurrency investments to enhance resilience.
Sheshadri Chatterjee, Tomáš Klieštik, Zuzana Rowland, Martin Bugaj
Research background: Internet of Things devices and sensors, artificial intelligence-based digital asset trading and digital twin-based extended reality technologies, and autonomous robotic and enterprise resource planning systems can be leveraged in 3D semantic scene completion and metaverse-based commercial transactions across Internet of Things-based business environments. Distributed ledger and enterprise business technologies, shop-floor digital twin synthetic data, and 3D simulation and visualization systems configure integrated multi-physics workflows in hyper-realistic immersive industrial environments for artificial intelligence-based business value. Digital twin-based Internet of Robotic Things, robotic swarm and multi-modal machine learning algorithms (with regard to enterprise total factor productivity), and virtual and augmented reality simulation technologies are pivotal in spatial planning processes. Industrial product data and manufacturing value chain management support digital twin-based virtual factory modeling in collaborative immersive 3D visualization environments. Purpose of the article: We show that interconnected business process management and metaverse economic organizational structures, immersive economic and entrepreneurial knowledge image-based modeling (for big data-driven product development processes), and remote autonomous equipment control and monitoring integrate digital twin-enabled 6G Tactile Industrial Internet of Things, deep reinforcement learning and image processing algorithms, and event-driven signal processing for collaborative economic value co-creation. Deep learning-based visual recognition and industrial extended reality technologies, 3D production management modeling, and Internet of Things industrial and mobile sensing networks are pivotal in production operation management, as deep learning-based multi-source data fusion assists autonomous industrial manufacturing processes across interactive 3D immersive business and synthetic manufacturing environments. Collaborative robotic cyber-physical production and generative Artificial Intelligence of Things-based systems (in terms of managerial business value), artificial intelligence-based perceptual and cognitive technologies, and spatial mapping and machine intelligence algorithms enhance manufacturing process visualization, as industrial big data sharing and interoperability are functional in 3D semantic scene completion for sustainable business and economic growth across big data-driven immersive virtual industrial manufacturing environments. Methods: We inspected Tracxn (the Industrial Metaverse section) for the first 100 companies in terms of Tracxn score for X-corn status (i.e., Minicorn, Soonicorn, or none), total equity funding (USD), and company stage (i.e., Seed, Funding Raised, Unfunded, Public, Acquired, Acqui-Hired, and Series A, B, C, D), and identified three main topics for analysis that would lead to tangible business outcomes. We examined the performance management of shop floor virtualization: connected digital twins increase production and logistics process optimization in production environments across the industrial metaverse, facilitating photorealistic production system 3D modelling and simulation. We appraised integrated diagnostic functionalities of real-time simulation implementation for error elimination and machine parameter adjustment in immersive planned production lines by synthetic image data sets and collaborative workflows. We determined digital twin-based data synthesis operational procedures and interconnected use cases across industrial scalable infrastructures for value chain efficiency. Findings & value added: We identified the specific integrated operational simulation functions and production tasks, key performance indicators of shop floor autonomous and value creation systems, and industrial process parameters for predictive quality and fault detection, resulting in production loss reduction by use of industrial metaverse technologies. By use of operational data with regard to the technological management of the selected companies, quantitative analysis determines how immersive collaborative business process and extended reality-driven industrial metaverse technologies lead to economic value co-creation across 3D digital twin factories and cyber-physical manufacturing enterprises. The main value added derived from our research is that cloud-based collaborative 3D visualization and neuromorphic computing systems, 6G sensing and holographic simulation technologies, and machine intelligence and environment awareness algorithms (for business performance and productivity) can be leveraged in machine vision-based defect prediction, detection, diagnosis, and management. Virtual reality space convergence and object connection operate in Internet of Things-based sensing device performance monitoring across Internet of Things-based business environments. Virtual assembly lines and manufacturing enterprises necessitate machine learning-based production forecasting techniques, 3D object detection and tracking, and industrial autonomous and cyber-physical production systems, supporting spatial computing and predictive maintenance algorithms in collaborative immersive virtual environments.
This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.
ВНЗ "Університет економіки та права "КРОК", Сергій Андрійчук, Володимир Кузьмінський, ВНЗ "Університет економіки та права "КРОК"
This article examines the macroeconomic aspects of the impact of cryptocurrencies on the money market, focusing on their relationship with traditional financial systems, monetary policy, and financial stability. The relevance of the study is due to the growing use of cryptocurrencies as a financial instrument and their integration into the global economy. In the last decade, digital assets have become widespread not only as a means of payment, but also as an element of an investment portfolio, which requires an in-depth analysis of their impact on economic processes. The purpose of the study is to assess the impact of cryptocurrencies on the money supply, monetary regulation mechanisms, and financial stability of states. The research methodology is based on the use of macroeconomic analysis, statistical methods, and a comparative analysis of different approaches to regulating the cryptocurrency market in different countries. Empirical data were used to identify the main trends in the interaction of digital assets with traditional financial systems and potential threats to the monetary policy of central banks. The results of the study indicate that cryptocurrencies can act as a factor that changes the traditional mechanisms of money market regulation. The decentralization of cryptocurrencies and their independence from state control pose new challenges to regulators. On the one hand, crypto-assets can promote financial inclusion and provide alternative methods of financing, on the other hand, they increase the level of volatility and create risks of financial instability. The article examines the role of stablecoins in international financial flows and their impact on the stability of the money supply. It is noted that stablecoins can act as an alternative to fiat currencies in the digital economy, which raises questions about their regulation and place in the monetary policy of states. Potential scenarios for the integration of cryptocurrencies into the modern financial system are investigated, in particular, through the development of central bank digital currencies (CBDCs), which can become an answer to the challenges posed to financial systems by the rapid development of blockchain technologies. Prospects for further research in this area include analyzing the effectiveness of regulatory approaches to controlling cryptocurrencies, studying the correlation between the Bitcoin exchange rate and macroeconomic indicators, and developing models for predicting the dynamics of the digital asset market. An extended study of the interaction of cryptocurrencies with the traditional banking system and their impact on international financial stability remains an important area of scientific research in the future.
Andrian Purwanto, Yudi Sumayadi, Tri Karyono, Enry Johan Jaohari
The rapid development of digital technology has significantly transformed the music industry, influencing various aspects from production and distribution to audience engagement. This article explores the implications of technological advancements, particularly the integration of artificial intelligence (AI), blockchain, non-fungible tokens (NFTs), and the Metaverse in the music sector. Using a qualitative approach with a literature study method, this research analyzes various sources and case studies to examine the impact of these technologies. The findings reveal that AI has revolutionized music creation by enabling automation in composition and production, enhancing efficiency and creative possibilities. However, this innovation also raises ethical debates regarding originality and copyright ownership. In parallel, blockchain and NFTs introduce a transparent and decentralized distribution model, allowing musicians to control their intellectual property and earnings without relying on intermediaries such as record labels. Despite these advantages, challenges remain, including limited public understanding of blockchain and market instability due to cryptocurrency fluctuations. urthermore, Metaverse-based virtual concerts and music experiences using VR technology present new opportunities for immersive and inclusive entertainment. However, replicating the emotional depth of live performances and ensuring accessibility to VR devices remain significant challenges. Overall, while these technologies offer promising innovations in the music industry, concerns related to regulation, ethics, and accessibility must be addressed. A balanced approach is necessary to fully harness the benefits of digital transformation while mitigating its potential drawbacks.
Robo-advisors have emerged as a transformative force in wealth management, leveraging artificial intelligence (AI) and machine learning to provide automated financial advisory services. This study conducts a bibliometric analysis of research on robo-advisors using data exclusively from the Scopus database and analyzed through VOSviewer. The findings reveal that research in this field has evolved from foundational discussions on fintech and artificial intelligence to advanced themes such as machine learning, decentralized finance, and algorithmic transparency. The keyword analysis highlights "wealth management," "fintech," and "machine learning" as central themes, while the co-authorship network indicates strong interdisciplinary collaboration among researchers. Additionally, the study identifies key regulatory and ethical challenges, including data privacy, fiduciary responsibility, and algorithmic bias, which require further investigation. The discussion explores the technological advancements, investor behavior, and regulatory landscape shaping the future of robo-advisory services. This research contributes to the growing academic discourse by mapping the intellectual structure of robo-advisor studies and suggesting future research directions, particularly in the areas of explainable AI (XAI), blockchain integration, and personalized financial advisory models.
This article examines the transformative impact of Agentic Process Automation (APA) on modern business workflows, highlighting the evolution from traditional Robotic Process Automation to autonomous intelligent systems. The article establishes APA as a paradigm shift that transcends the limitations of conventional automation approaches through self-governing agent models capable of adaptive decision-making. Through comprehensive analysis spanning architectural foundations, comparative capabilities, multi-agent collaboration frameworks, and real-world implementations, this article demonstrates how APA systems deliver superior performance in dynamic business environments. Key aspects explored include decentralized intelligence, machine learning integration, ethical governance frameworks, and strategic implementation methodologies. Case studies across financial services, healthcare, and manufacturing sectors provide empirical evidence of APA's operational benefits, while also highlighting implementation challenges and mitigation strategies. The article reveals that organizations implementing agentic systems achieve significant improvements in process efficiency, adaptability, and cost optimization compared to traditional automation approaches, particularly for complex workflows requiring judgment and contextual understanding. This article provides valuable insights for organizations navigating the transition toward intelligent automation and offers a structured framework for evaluating APA readiness, implementation priorities, and governance considerations within enterprise environments
In our previous research, we addressed the problem of automated transformation of models, represented using the business process model and notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing the modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern on the basis of the successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction’s BPMN model. The amended BPMN model is then transformed into a new smart contract, ensuring consistent data and logic transitions. We previously developed a tool, called TABS+, as a proof of concept (PoC) to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+ R , developed by extending the TABS+ tool, to allow the repair of smart contracts.
Mother of all Bitcoins and cryptocurrencies but why haven’t we seen the latent potency this technology could bring to the world in this era. Blockchain is THE revolutionary change which is going to predominantly realign every facet of business and technology. Beginning from storage of data till public voting, a blockchain can drastically transform day to day activities even to a laymen. It is predicted at least 10% of global GDP (Gross Domestic Product) will be stored on a blockchain. Technically briefing, this is not just turning to be an aid to organizations and society but it is employing into a comprehensive replacement of the whole extensive process. Industry 4.0, Web3.0, Decentralized Autonomous Organizations, Smart Contracts are latest modernizations going to dictate the world very soon.
In Ethereum, private transactions, a specialized transaction type employed to evade public Peer-to-Peer (P2P) network broadcasting, remain largely unexplored, particularly in the context of the transition from Proof-of-Work (PoW) to Proof-of-Stake (PoS) consensus mechanisms. To address this gap, we investigate the transaction characteristics, (un)intended usages, and monetary impacts by analyzing large-scale datasets comprising 14,810,392 private transactions within a 15.5-month PoW dataset and 30,062,232 private transactions within a 15.5-month PoS dataset. While originally designed for security purposes, we find that private transactions predominantly serve three distinct functions in both PoW and PoS Ethereum: extracting Maximum Extractable Value (MEV), facilitating monetary transfers to distribute mining rewards, and interacting with popular Decentralized Finance (DeFi) applications. Furthermore, we find that private transactions are utilized in DeFi attacks to circumvent surveillance by white hat monitors, with an increased prevalence observed in PoS Ethereum compared to PoW Ethereum. Additionally, in PoS Ethereum, there is a subtle uptick in the role of private transactions for MEV extraction. This shift could be attributed to the decrease in transaction costs. However, this reduction in transaction cost and the cancellation of block rewards result in a significant decrease in mining profits for block creators.
This study provides a bibliometric analysis of the research trends in financial derivatives within the banking and finance literature. By examining citation patterns, co-authorship networks, and keyword co-occurrences, the study identifies key research themes and their evolution over time. The analysis reveals the central role of derivatives in risk management and financial stability, particularly in the wake of financial crises. It highlights the growth of computational techniques in derivatives pricing and risk management, with an increasing focus on advanced models and simulations. The study also explores the emerging influence of blockchain technology and decentralized finance in reshaping the derivatives landscape. The bibliometric map underscores the global nature of financial derivatives research, with significant contributions from the United States, China, and the United Kingdom. The study provides valuable insights for scholars, practitioners, and policymakers, suggesting areas for further research, particularly in regulatory frameworks, pricing models, and the integration of new technologies in the derivatives market.
The most dependable service on the planet is blockchain. It functions like a ledger to enable distributed transaction processing. The internet of things (IoT), financial services, non-financial services, and a plethora of other sectors are only a few of the domains in which blockchain technology finds application. Without the requirement for central authority verification, blockchain combines a distributed ledger and a distributed database. The many consensus methods, blockchain challenges, and their extent are covered in this study. This technology is still facing numerous obstacles that need to be resolved, like scalability. The ripple protocol consensus algorithm (RPCA), delegated proof of stake (dPOS), proof of work (POW), proof of stake (POS), stellar consensus protocol (SCP), and proof of importance (POI) are the consensus algorithms behind the technology known as blockchain. This Review discusses the fundamental idea behind blockchain technology as well as different mining techniques, consensus problem algorithms for consensus, and performance-based comparison algorithms, blockchain’s benefits, the architecture of Blockchain and applications of blockchain such as ATM.
Middlemen handle disputes during the payment process to ensure that it remains seamless and efficient in systems that are highly distributed. It boils down to primarily addressing common challenges like fraud, transaction speed, and the need for transparency. This paper presents a web-based payment system designed to facilitate the transfer of cryptocurrency over the internet without relying on any intermediaries by leveraging ledger-based distributed technology, automated agreements, and protection measures. This ensures seamless operations with fewer intermediaries while maintaining efficiency and safeguarding transactions. Our implementation consists of a simple web application built using React, Node.js, and CSS for a responsive front end. The backend incorporates autonomous agreements and is tested using a simulated blockchain network, facilitating trustless record-keeping. MetaMask compatibility allows users to link wallets and securely execute digital asset exchanges, reducing transaction costs and ensuring visible, verifiable transfers. This paper contributes to the growing body of knowledge on open finance (DeFi) and serves as a cornerstone. Our website offers an operational example of these concepts, providing a realistic viewpoint on blockchain-based payments in real-world scenarios. While it establishes a protected structure, off-chain agreements are susceptible to coding flaws or exploitation. Poorly designed contracts can lead to financial losses if attackers identify and take advantage of weaknesses. Performing thorough security evaluations and adopting best practices in contract development are crucial to ensuring strong protection against potential threats. Key Words: Blockchain Payments, Cryptocurrency Transactions, Decentralized Finance (DeFi), Smart Contracts, Distributed Ledger Technology
The blockchain based smart contracts allow the creation of peer-to-peer lending in a decentralized finance model called DeFi. While Aave, Compound, and MakerDAO make it easier to gain access to capital and do away with middlemen, security breaches are highly likely to occur. This study analyzes the smart contract vulnerabilities such as reentrancy attacks, oracle manipulation, flash loan exploits, are systematically highlighted and their impact on projects in the market. Furthermore, it completes assessment beyond the security focus of liquidity volatility, regulatory uncertainty and fragmented risk management framework. A systematic literature review was adopted in the study with peer reviewed journal, industry report as well as case studies of past DeFi exploits. The key vulnerabilities, risk assessment methods, and mitigation frameworks are dealt as a theme. According to findings, although smart contract security has improved, DeFi is still very prone to exploitation for the lack of centralized oversight and standardised security measures. The study also brings our attention to the fact that risks in smart contract need continuous smart contract audits, formal verification schemes, and decentralized insurance mechanisms as well as regulatory collaboration. For the sustainable growth of DeFi lending platforms, such a balance should be made possible between technological security measures and improved governance and regulatory frameworks. The increased security mechanisms will increase the user trust and make decentralized lending an alternative to traditional financial systems.
Background: Even while traditional Raft is effective at leader election and log replication, it is not appropriate for sensitive applications like supply chains, financial systems, or healthcare because it lacks built-in privacy safeguards. Materials and Methods: A privacy-preserving Raft consensus method is proposed to solve the privacy issues that occur when private information is transferred between nodes in a distributed system such as a blockchain. Raft itself, by default, does not provide any steps toward ensuring data confidentiality during consensus. By employing privacy-preserving cryptographic techniques like homomorphic encryption and zero-knowledge proofs, nodes can reach consensus while keeping sensitive data private. Results: Traditional Raft performs much better in scenarios where performance matters, while Privacy-Perving Raft works better in a sensitive application to privacy (the average of write throughput is 5% lower than that of traditional Raft) and CPU is 40-60%. Conclusion: Based on the gained privacy by some computational costs, it will be valid to draw the conclusion that this works for privacy-sensitive applications within decentralized systems with these performance and security analyses.
This proposal outlines an innovative strategy to integrate Web 3.0 technologies — specifically Non-Fungible Tokens (NFTs) — into Clash of Clans. By enabling players to own unique NFT-based avatars with special superpowers, Supercell can tap into the rapidly expanding blockchain gaming economy. This model would not only enhance player engagement and loyalty but also create new and sustainable revenue streams through NFT sales, royalties, and marketplace transactions.