Blockchain Papers

Zhan Wang, Xiang Gao, GU Jiahao

No abstract is available for this record.

Gaimei Gao, Zilu Li, Lizhong Jin, Chunxia Liu · 6 authors

Smart contracts are at the core of blockchain technology, but the cost of fixing their security vulnerabilities is high, making pre-deployment vulnerability detection crucial. Existing methods rely on fixed rules, which have limitations in accuracy and scalability, and their efficiency decreases with the complexity of the rules. Neural-network-based methods can identify some vulnerabilities but are inefficient in multi-vulnerability scenarios and depend on source code. To address these issues, we propose a multi-vulnerability-based smart contract detection method called RTMS. RTMS takes bytecode as input, disassembles it into opcodes, uses the gas consumed by the contract for data slicing, and extends the length of input opcodes through a layered structure. It employs a weighted binary cross-entropy (BCE) function to handle data imbalance and combines channel-sequence attention mechanisms to extract vulnerability correlation features. By using transfer learning, it reduces training parameters and computational costs. Our RTMS model can detect multiple vulnerabilities simultaneously, enhancing detection accuracy and efficiency. In experiments with 100,000 real contract samples, the model achieved a Jaccard coefficient of 0.9312, a Hamming loss of 0.0211, and an F1 score that improved by about 11 percentage points compared to existing models, demonstrating its superiority and stability.

Jagdish Jangid Sachin Dixit

Financial technology (fintech) faces growing demands for faster data processing, reduced delays, and better security as the sector rapidly advances. Current centralized systems are vulnerable to various threats including data manipulation, service outages, and security breaches that can compromise financial transactions. This research examines how combining blockchain technology, smart contracts, and machine learning could solve key challenges in fintech-related to security, transparency, and operational performance. The study also considers how these technologies affect regulatory compliance, legal frameworks, and ethical oversight. The research methodology involves analyzing ten years of literature on blockchain in fintech, specifically focusing on decentralized ledgers, automated smart contracts, and machine learning for data analysis. The findings are presented visually through diagrams and data visualizations that demonstrate improvements in operations, security, and cost efficiency. The study shows that blockchain provides transparent, secure financial record-keeping through its decentralized structure. Smart contracts help reduce costs and make financial services more accessible to underserved groups by automating processes. Machine learning enhances these blockchain applications by enabling predictive analysis and data-driven choices. While progress has been significant, there are still obstacles to overcome, particularly in developing governance frameworks that ensure ethical use and regulatory compliance. This analysis contributes to new understanding by examining how blockchain and machine learning work together in fintech, with special attention to previously understudied areas like operational efficiency, security, and regulatory compliance. The research outlines how these technologies can transform finance while providing practical solutions to current challenges, working toward a more secure, inclusive, and efficient financial system.

Amjad Almaghthawi, Wael M. S. Yafooz

Blockchain (BC) technology and Artificial Intelligence (AI) are promising areas with potential for powerful, secure, and decentralized applications in various sectors. Also, LLMs, are essential advanced machine learning frameworks, which are now used in various applications, including customer service, chatbots, code generation, and language translation. In terms of software development, LLMs are being investigated as a potential way to create smart contracts, focusing on their ability to emulate human programming techniques. For that purpose, this study reviews the capabilities and limitations of Large Language Models (LLMs) like GPT-3.5, PaLM2, and Code Llama in detecting vulnerabilities in Solidity smart contract code.

Yackolley Amoussou-Guenou, Maurice Herlihy, Sucharita Jayanti, Maria Potop-Butucaru · 5 authors

Many of the problems that arise in the context of blockchains and decentralized finance can be seen as variations on classical problems of distributed computing. The smart contract model proposed here is intended to capture both the similarities and the differences between classical and blockchain-based models of distributed computing. The focus is on cross-chain protocols in which a collection of parties, some honest and some perhaps not, interact through trusted smart contracts residing on multiple, independent ledgers. While cross-chain protocols are capable of general computations, they are primarily used to track ownership of assets such as cryptocurrencies or other valuable data. For this reason, the smart contract model differs in some essential ways from familiar models of distributed and concurrent computing. Because parties are potentially Byzantine, tasks to be solved are formulated using elementary game-theoretic notions, taking into account the utility to each party of each possible outcome. As in the classical model, the parties provide task inputs and agree on a desired sequence of proposed asset transfers. Unlike the classical model, the contracts, not the parties, determine task outputs in the form of executed asset transfers, since they alone have the power to control ownership.

Asad Abbas, Ghulam Hussain Khan Zaigham, Arslan Ali Raza

Asset tokenization is about to reshape the world of investment and finance due to its enormous potential for providing customized solutions to those aspiring to be part of a financial ecosystem. Tokenization is the process of transferring large assets into a large number of small tokens of ownership, provided with a unique digital identity and presence in a blockchain with its immutability and interoperability. It is incredibly secure, remarkably liquid, significantly cost-effective, reasonably transparent, easily accessible and very broad in spectrum and scope. It encompasses all financial and real assets, ranging from commodities to securities, real estate, artworks, sports, the corporate sector and even collectibles. It can provide convenient, customized and cost-effective access to anyone in the digital world of blockchain technology. It also removes the role of any intermediary, avoids delays in transferring ownership of assets and subsequently eliminates transaction costs. Due to its immense potential of distributed ledger technology (DLT), it can manage transactions involving huge sums in a transparent and speedy manner. Despite its massive potential, there are some challenges that potential entrants must face, hindering the unleashing of this potential. This chapter attempts to unfold some of the prospects and challenges of asset tokenization which the decentralized financial world of tokenization and its players face, including the complexities associated with the codification and creation of digital tokens, regulatory challenges, fear of entering the universe of virtual reality, a lack of tangible holding of assets, the rapid evolution and development of digital markets, over-coming trust deficits among counterparties, finding appropriate assets and the discovery and integration of demand–supply mechanisms.

Xiong Zenggang, Lou Qiangqiang, Youfeng Li, Hao Chen · 7 authors

No abstract is available for this record.

Jun Kevin, Pujianto Yugopuspito

Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, leading to high false-positive rates and an inability to detect dynamic vulnerabilities. This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) to enhance the accuracy and efficiency of smart contract auditing. By integrating domain-specific knowledge from ERC standards and employing advanced techniques such as QLoRA for efficient fine-tuning, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither, as well as zero-shot large language model (LLM) prompting methods such as GPT-3.5 and GPT-4. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities, including reentrancy and access control issues. This research advances smart contract security by offering a scalable and effective auditing solution, supporting the secure adoption of decentralized applications.

Jisu Jang, Jiyun Kang

This research explores the impact of Non-Fungible Token (NFT) authentication on purchase intention in new and pre-loved luxury markets, grounded in warranting theory and institution-based trust theory. Using a two-study online experimental design (Study 1: new luxury market, Study 2: pre-loved luxury market), both studies used a one-factor (NFT authentication) and two-level (yes or no) design and PROCESS macro Model 6 for serial mediation analysis. The results from Study 1 indicate that NFT authentication enhances purchase intention through increased warranting value and structural assurance. Study 2 confirmed these serial mediating effects and revealed a direct significant impact of NFT authentication in the pre-loved luxury market, which was not significant in the new luxury market. This study highlights the importance of NFT authentication in enhancing consumer trust and purchase intention in both new and pre-loved luxury markets.

Yuchen Liu

The distinctive attributes of blockchain technology, including decentralization, traceability, tamper resistance, transparency, security, and privacy, have paved the way for remarkable achievements across various domains, encompassing financial transactions, healthcare systems, agricultural intelligence, and energy upgrades. Notably, within the domain of blockchain-based supply chain finance, it has emerged as a potent solution to longstanding challenges, ranging from inaccurate risk assessment to information asymmetry and the mismatch between supply and demand service offerings. It has furnished the conventional supply chain ecosystem with a reliable and robust mechanism. This study addresses the multifaceted role and impactful applications of blockchain technology, elucidates the technical underpinnings of its implementation, and sheds light on the prevailing challenges that demand resolution within the sphere of supply chain finance. Furthermore, this paper also presents insights and prospects from an enterprise perspective, offering valuable guidance to future scholars venturing into this domain.

Deepak Mishra, Pawan Singh Mehra

Blockchain was originally developed for digital currency, but the capability of technology extends quite far beyond cryptocurrency. The Smart Contracts, self-executing and unalterable digital agreements, further extended the capability. While smart contracts offer various possibilities, they are also vulnerable to a variety of attacks such as integer overflows, reentrancy attacks and denial-of-service (DoS) attacks. This research paper reviews the currently available tools that are used to detect these vulnerabilities, such as fuzz testing and static code analysis. Limitations of these tools such as scalability issues, high false positives, high false negatives and difficulties in handling large and complex contracts are highlighted. To address all these problems, this paper introduces a tool named ‘TotalSol’, a new tool that combines various analysis methods into a single comprehensive solution with a scalable platform. Using analysers like Slither, Aderyn and SmartCheck, TotalSol solves the accuracy, speed and scalability problems while minimizing false negatives and false positives. It creates a platform for developers and researchers to add new methods and detection tools in their underlying architecture without much alteration. A smart contract downloader tool is proposed to gather real-time contract data for better analysis. This research targets to deliver a more effective and an automated way to analyse the smart contracts. This ensures high security standards and reduces the risks involved with vulnerabilities in the decentralized applications.

С.Г. Валентинов

цель исследования – проанализировать влияние и будущие развитие альтернативного варианта централизированной страховой отрасли, который в научной литературе именуется как децентрализированное страхование. Децентрализованное страхование, также известно как страхование на основе блокчейна, является революционной концепцией, возникшей с появлением технологии блокчейн. Произвести разбор пробелов дизайна продуктов децентрализированного страхования в рыночной практике страхования и теоретических моделей распределения рисков в литературе. И на основе данного анализа и разбора представить общую структуру модели взаимного страхования в здравоохранении, которую можно было бы применять для разработки на платформах децентрализованных финансов, и математическую основу для описания общих свойств. Так же дать общее представление о понятии (де)централизации. вытекающее из архитектуры блокчейн технологии. Методологической базой исследования служат общенаучные методы исследования: логика, синтез, анализ, индукция, дедукция, а также агентно-ориентированный подход к анализу страховой отрасли. Результаты. Определен один из подходов к разработке модели децентрализированного страхования как части системы децентрализированных финансов и многоагентному моделированию страховых взаимодействий в направлении их улучшения качественности и безопасности. В рамках предложенной модели установлено, что критерием эффективности ее алгоритма служит низкое потребление ресурсов на поддержку платформы, вектор работы в целом направлен в сторону страхователей в отличии от централизированного страхования, где он направлен в сторону страховой компании и все свойства присущи блокчейн технологиям. Вывод. По мере того, как эта концепция децентрализированного страхования развивается и созревает, она может изменить страховую отрасль, расширяя возможности отдельных лиц и организаций для более эффективного управления рисками, одновременно укрепляя доверие и подотчетность среди участников. the purpose of the study is to analyze the impact and future development of an alternative version of the centralized insurance industry, which is referred to in the scientific literature as decentralized insurance. Decentralized insurance, also known as blockchain-based insurance, is a revolutionary concept that emerged with the advent of blockchain technology. To analyze the gaps in the design of decentralized insurance products in the insurance market practice and theoretical models of risk distribution in the literature. And based on this analysis and analysis, to present the general structure of the mutual insurance model in healthcare, which could be used for development on decentralized finance platforms, and a mathematical basis for describing common properties. Also give a general idea of the concept of (de)centralization. stemming from the architecture of blockchain technology. The methodological basis of the research is general scientific research methods: logic, synthesis, analysis, induction, deduction, as well as an agent-oriented approach to the analysis of the insurance industry. Results. One of the approaches to the development of a model of decentralized insurance as part of a system of decentralized finance and multi-agent modeling of insurance interactions in the direction of improving their quality and safety is defined. Within the framework of the proposed model, it was found that the criterion for the effectiveness of its algorithm is low resource consumption to support the platform, the vector of work is generally directed towards policyholders, unlike centralized insurance, where it is directed towards the insurance company and all the properties are inherent in blockchain technologies. Conclusion. As this concept of decentralized insurance develops and matures, it can transform the insurance industry by empowering individuals and organizations to manage risks more effectively, while strengthening trust and accountability among participants.

Ashimiyu Nafiu, Salaam Olawale Balogun, Courage Oko-Odion, Olanrewaju Olukoya Odumuwagun

The complexities of modern financial markets, characterized by heightened volatility and uncertainty, have necessitated the evolution of advanced risk management strategies. As global markets become increasingly interconnected, financial institutions, investors, and policymakers face unprecedented challenges in identifying, assessing, and mitigating risks. Effective risk management has emerged as a cornerstone of financial stability, requiring a blend of traditional methods and innovative tools. This paper explores comprehensive strategies for navigating volatility in complex financial environments, addressing systemic, credit, market, and operational risks. Traditional approaches, such as portfolio diversification and value-at-risk (VaR) modelling, remain foundational but are now complemented by cutting-edge technologies, including artificial intelligence (AI), machine learning (ML), and big data analytics. These tools enable real-time monitoring, predictive analytics, and stress testing, enhancing the capacity to anticipate and respond to emerging threats. Additionally, the integration of blockchain technology offers improved transparency and resilience in financial transactions, further mitigating systemic vulnerabilities. Case studies from diverse sectors highlight the practical applications of these strategies, illustrating how robust risk management frameworks can minimize losses, enhance profitability, and ensure regulatory compliance. The paper also examines the role of regulatory frameworks in shaping risk management practices and emphasizes the importance of a proactive, adaptive approach in navigating volatile market conditions. By combining traditional methodologies with technological advancements, financial institutions can build resilient systems capable of withstanding shocks and fostering long-term stability. This paper concludes by identifying emerging trends, such as quantum computing and decentralized finance, as transformative forces likely to redefine risk management in the future.

Sofia Bobadilla, Monica Jin, Martin Monperrus

Automated Program Repair (APR) for smart contract security promises to automatically mitigate smart contract vulnerabilities responsible for billions in financial losses. However, the true effectiveness of this research in addressing smart contract exploits remains uncharted territory. This paper bridges this critical gap by introducing a novel and systematic experimental framework for evaluating exploit mitigation of program repair tools for smart contracts. We qualitatively and quantitatively analyze 20 state-of-the-art APR tools using a dataset of 143 vulnerable smart contracts, for which we manually craft 91 executable exploits. We are the very first to define and measure the essential "exploit mitigation rate" , giving researchers and practitioners a real sense of effectiveness of cutting edge techniques. Our findings reveal substantial disparities in the state of the art, with an exploit mitigation rate ranging from a low of 29% to a high of 74%. Our study identifies systemic limitations, such as inconsistent functionality preservation, that must be addressed in future research on program repair for smart contracts.

Nidhal Mgadmi, Nozha Erragcha

No abstract is available for this record.

Hongyu Yan, Chunjie Cao, Zhaoxing Jing, Haochen Li · 8 authors

No abstract is available for this record.

Finn Jarvi, Cash Hilinski

No abstract is available for this record.

Teresa Goody Guillén

No abstract is available for this record.

Ashimiyu Nafiu, Salaam Olawale Balogun, Courage Oko-Odion

The complexities of modern financial markets, characterized by heightened volatility and uncertainty, have necessitated the evolution of advanced risk management strategies.As global markets become increasingly interconnected, financial institutions, investors, and policymakers face unprecedented challenges in identifying, assessing, and mitigating risks.Effective risk management has emerged as a cornerstone of financial stability, requiring a blend of traditional methods and innovative tools.This paper explores comprehensive strategies for navigating volatility in complex financial environments, addressing systemic, credit, market, and operational risks.Traditional approaches, such as portfolio diversification and value-at-risk (VaR) modelling, remain foundational but are now complemented by cutting-edge technologies, including artificial intelligence (AI), machine learning (ML), and big data analytics.These tools enable real-time monitoring, predictive analytics, and stress testing, enhancing the capacity to anticipate and respond to emerging threats.Additionally, the integration of blockchain technology offers improved transparency and resilience in financial transactions, further mitigating systemic vulnerabilities.Case studies from diverse sectors highlight the practical applications of these strategies, illustrating how robust risk management frameworks can minimize losses, enhance profitability, and ensure regulatory compliance.The paper also examines the role of regulatory frameworks in shaping risk management practices and emphasizes the importance of a proactive, adaptive approach in navigating volatile market conditions.By combining traditional methodologies with technological advancements, financial institutions can build resilient systems capable of withstanding shocks and fostering long-term stability.This paper concludes by identifying emerging trends, such as quantum computing and decentralized finance, as transformative forces likely to redefine risk management in the future.

Elena Korobova, Dean Fantazzini

No abstract is available for this record.

Burcu Kapar, Mabruk Billah, Mohammad Talha

No abstract is available for this record.

Archana Pattabhi

The increased volatility in the financial world, alongside the dynamics that characterize the global economy, have motivated the concept of Financial Digital Twins (FDTs) as AI-based, virtual models of the banking systems aimed to optimize risk management, fraud detection, and operational performance. With real-time data, operational research for risk assessment, and artificial intelligence systems, FDTs are useful in helping financial institutions anticipate risks, manage their decision systems, and design a better model for compliance with regulations. In the following paper, a literature review will look into using AI and ML to enhance the effective development of Financial Digital Twins. It discusses some modelling approaches that can be used in the industry, including Monte Carlo simulations, agent-based modelling, and the scenario approach, which help financial institutions model the market risk and credit performance alongside conditions in an economic downturn. Moreover, it reveals that AI can benefit credit scoring, fraud detection, and stress testing to increase the evaluation of risks. Still, when it comes to the drawbacks of the FDT, they are all associated with data protection, data processing, scalability, compliance issues, and AI model quality, respectively. This paper also covers new technologies like quantum computing, blockchain integration, and decentralized finance (DeFi), which have enhanced the existing dimensions of risk management in finance. Financial institutions must meet these challenges and adapt high technologies to advance banking systems to be financially sound, more informed, and more protective of banking systems

Yu Li, Jian Jiao, Shi Ping

No abstract is available for this record.

Liyuan Zhang, Limian Ci, Yonghong Wu, Benchawan Wiwatanapataphee

The rapid expansion of blockchain technology has created both opportunities and challenges in financial markets, particularly in the investment of blockchain-based real estate tokens. Unlike traditional financial assets, these investments exhibit high volatility, decentralized trading mechanisms, and complex transaction fee structures, all of which significantly influence portfolio management strategies. This study tackled the core issue of portfolio optimization in blockchain asset markets by incorporating both proportional and fixed transaction costs, factors often overlooked in conventional models. To address this, we proposed a multi-period investment optimization framework that leveraged Lagrange multipliers and dynamic programming to determine optimal asset allocation. A key feature of our model was its ability to define an optimal no-trade region, balancing transaction costs with investment returns under varying fee structures. Through numerical experiments, we analyzed how different levels of transaction costs impacted trading frequency, risk exposure, and portfolio efficiency. Our findings indicated that higher transaction costs expanded the no-trade region, reducing trading frequency, while lower costs encouraged more frequent rebalancing. Additionally, we highlighted the practical benefits of blockchain real estate tokenization, including lower investment barriers, enhanced market liquidity, and seamless cross-border transactions. By providing a robust theoretical and empirical framework, this research offered valuable insights for investors navigating blockchain-based financial markets and contributed to the broader discourse on decentralized finance (DeFi) and tokenized real estate investments.