Blockchain Papers

Oleksandr Oliіnyk

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Yuqiang Sun

Smart contract technology has witnessed rapid evolution and widespread adoption across diverse industries. However, with the immutable nature of blockchain-deployed contracts, vulnerabilities—especially those embedded in complex business logic—pose severe security risks. Traditional static analysis tools have struggled to accurately capture such vulnerabilities, prompting exploration into novel techniques that integrate large language models (LLMs), static analysis, and property-based testing. Firstly, we proposed a unified evaluation framework called LLM4Vuln, systematically decouples and assesses LLMs’ intrinsic vulnerability reasoning from external aids like knowledge enrichment and context retrieval. Evaluated on 294 code snippets spanning Solidity, Java, and C/C++ over 3,528 scenarios, LLM4Vuln not only elucidated the impacts of various enhancements but also uncovered 14 zero-day vulnerabilities in real-world projects, demonstrating both practical value and potential for significant security improvements. Secondly, building on these insights, we proposed GPTScan, the first tool to integrate GPT with static analysis for smart contract logic vulnerability detection. By decomposing each vulnerability into specific scenarios and properties, GPTScan employs GPT to identify critical code elements and then confirms these findings through static analysis. This hybrid approach achieves high precision on token contracts, maintains acceptable performance on large-scale projects, and delivers an overall recall above 70%, thereby effectively identifying vulnerabilities often overlooked by human auditors. Thirdly, to extend the scope of detectable vulnerabilities, we designed PropertyGPT, a framework leverages retrieval-augmented property generation. By harnessing LLMs’ in-context learning abilities, PropertyGPT generates compilable, context-appropriate, and verifiable properties for formal verification of smart contracts. Experimental results demonstrate an 80% recall relative to ground truth, with the framework successfully detecting multiple CVEs and uncovering several zero-day vulnerabilities, which have resulted in substantial bounty rewards. Fourthly, to address the detection of reentrancy vulnerabilities, we developed ReeSem. ReeSem combines static analysis with semantic understanding through LLMs. Its three-stage detection pipeline—filtering external calls, analyzing affected state variables, and semantically recognizing reentrancy guards—delivers an F1 score of 75.14%, outperforming state-of-the-art baselines significantly. ReeSem’s ability to generate consistent attack paths in real-world scenarios underscores its practical applicability and robustness. Fifthly, complementing the data-driven methods, ZepScope focuses on static analysis by mining constraints directly from official smart contract implementations, specifically, from those provided by OpenZeppelin. Through its MINER and CHECKER components, ZepScope extracts both explicit and implicit security checks and validates their enforcement in real-world contracts. This approach achieves an impressive accuracy of 89.67% across tens of thousands of contracts, offering critical insights into common code practices and potential security pitfalls. Collectively, these contributions, LLM4Vuln, GPTScan, PGPT, ReeSem, and ZepScope, form a comprehensive framework for enhancing vulnerability detection in smart contracts. By synergistically integrating large language models, static analysis, security constraints mining and property-based testing, this work advances the state-of-the-art in secure code auditing and provides valuable methodologies for developers, auditors, and the broader security community.

Xihui Haviour Chen, Provash Kumer Sarker, Chi Keung Marco Lau

No abstract is available for this record.

David Krause

No abstract is available for this record.

Olayimika Oyebanji

No abstract is available for this record.

Murali Malempati

No abstract is available for this record.

Oleg Klym, Tetiana Melnyk

The complexity of business operations in the global economy is rising, necessitating new approaches to contractual relationship management. This article explores the potential of smart contracts as a transformative tool for international business. By utilizing blockchain technology, smart contracts can automate agreements, reduce transaction costs, and enhance security and efficiency. Initially developed on the Ethereum network, smart contracts have since evolved with the introduction of new blockchain platforms. This paper examines the theoretical background of smart contracts, their practical applications in international business activities, and their impact on economic efficiency. Despite high costs of development, insufficient legal frameworks, and trust-related concerns, the findings suggest that smart contracts hold great potential to optimize international economic activities by increasing transparency, reducing risks, and facilitating decentralized governance.

Michael Olumuyiwa Adesuyi, Olawole Akomolafe, Babajide Oluwaseun Olaogun, Victor Ukara Ndukwe · 5 authors

The global payment infrastructure faces unprecedented challenges from escalating geopolitical tensions, economic sanctions, cyber warfare, and technological disruptions that threaten the stability and continuity of cross-border financial transactions. This study presents a comprehensive resilience and continuity model designed to enhance the robustness of global payment systems against geopolitical risks while maintaining operational efficiency and regulatory compliance. Through systematic analysis of existing payment infrastructure vulnerabilities and emerging risk factors, this research develops a multi-layered framework incorporating distributed ledger technologies, artificial intelligence-driven risk assessment, and adaptive governance mechanisms to ensure payment system continuity during geopolitical crises. The proposed model integrates blockchain-based interoperability protocols, real-time threat intelligence systems, and dynamic routing algorithms that enable automatic rerouting of payment flows when traditional channels are compromised. Key innovations include a geopolitical risk scoring system that continuously monitors political stability indicators, regulatory changes, and sanctions regimes across jurisdictions, providing early warning capabilities for payment service providers. The framework also incorporates federated learning approaches for cross-border fraud detection while preserving data sovereignty requirements mandated by different regulatory jurisdictions. Implementation analysis reveals that the proposed resilience model can reduce payment disruption incidents by 67% during moderate geopolitical tensions and maintain 85% operational capacity even during severe international crises. The system's adaptive architecture enables real-time reconfiguration of payment routes based on geopolitical risk assessments, ensuring compliance with evolving sanctions regimes while minimizing transaction delays. Cost-benefit analysis demonstrates that implementing this resilience framework reduces operational losses from payment disruptions by approximately $2.4 billion annually across major financial institutions. The research methodology employed mixed-methods approaches, combining quantitative analysis of historical payment disruption data from 2015-2024 with qualitative assessment of expert opinions from central banks, payment processors, and fintech institutions across 15 countries. Validation testing using Monte Carlo simulations and stress testing scenarios confirms the model's effectiveness in maintaining payment continuity under various geopolitical crisis scenarios including trade wars, financial sanctions, and regional conflicts. This study contributes to the literature by providing the first comprehensive framework specifically designed to address geopolitical risks in global payment infrastructure, offering practical implementation guidelines for financial institutions, central banks, and payment service providers seeking to enhance their operational resilience in an increasingly volatile geopolitical environment.

Cristina Poncibò

No abstract is available for this record.

Amrita Jain, Savi Jain, Shruti Lashkari, Sweta Gupta · 5 authors

The overall objective of the study is to understand the current status of blockchain applications and evaluate their ability to satisfy the growing demand for blockchain knowledge in the applications industry.In order to determine which would be the superior option in each situation, it also evaluated the respective advantages of Ethereum and Hyperledger.The study's extensive data set allowed it to offer priceless insights into the intricate workings of a blockchain application.The materials used included reports, journals, and periodicals.The "smart contract" refers to a digital transaction that runs on its own, logs the pertinent dynamic activity on a distributed ledger, and uses predefined criteria to demonstrate its legitimacy.The key component of a blockchain that enables its use as a platform for use cases beyond currency is a smart contract.Voting, education, entertainment, real estate, the Internet of Things (IoT), The development of blockchain technology has advanced significantly in recent years, with a particular emphasis on smart contracts; yet, little research has been done on the idea.Notwithstanding the many advantages of smart contracts, a number of obstacles have prevented their widespread use, including as security holes, coverage gaps, and the difficulties of lawfully enforcing contracts.

A. S.

No abstract is available for this record.

Emanuele Antonio Napoli, Valentina Gatteschi, Alberto Cannavò, Davide Calandra · 6 authors

No abstract is available for this record.

Arghya Mukherjee, Tyler Moore

Thousands of cryptocurrency coins and tokens have been introduced in recent years, with each purporting to offer a unique take on disrupting traditional financial instruments. Most fail to attract significant investment, but some grow quite valuable for at least a short time. This paper focuses on so-called "crypto unicorns'', which reach a market capitalization of at least $1 billion at some point during their lifetimes. 37 coins and 139 tokens have reached unicorn status. However, only 15 coins and 35 tokens retain market capitalizations exceeding $1 billion at end of our study, with 6 coins and 31 tokens falling below $100 million. We empirically examine the factors that influence the relative success or failure of crypto unicorns. Using regression analysis, we find that bitcoin price, the type of service offered by the coin or token, having an ICO and social media activity all affect success.

Vasiliki Basdekidou

Abstract Digital transformation and the adoption of blockchain in corporate operations and financial services introduces a new set of significant policy issues and concerns about security, competition, and regulatory rules ensuring healthy competition and level playing fields in the FinTech business and financial ecosystem. From the disrupting adoption of blockchain and the impact of FinTech, one possible outcome concerning market competitiveness, concentration, and competition is a business and financial world consisting of numerous specialized businesses and just a few major providers. Hence, to handle trade-offs between stability, trust, integrity, knowledge, information, data sharing, competitiveness, efficiency, consumer protection, cyber-hacking, security, and privacy, authorities must collaborate across financial regulation, competition, and industry regulatory organizations. This paper follows a simple literature review methodology for demonstrating knowledge and understanding of the academic literature on the disruptive power of FinTech and the importance of blockchain and distributed ledger technologies in digital transformation processes.

Shitao Wang

No abstract is available for this record.

Eliza Mik

No abstract is available for this record.

Qianyi Luo

Abstract This paper quantitatively analyses the development status and market share of cryptocurrencies by collecting relevant information and explores the correlation between the cryptocurrency market and the performance of China’s financial market and financial market pressure through the correlation analysis method. Using VAR model impulse analysis to portray the dynamic relationship between cryptocurrencies and the financial market during unexpected events can help show the risk changes of the cryptocurrency market more intuitively. The analysis shows that cryptocurrencies have entered a stage of explosive development, and by 2023, their overall market value will reach about $3 trillion. Among them, Bitcoin has a market share of 39.8%. The correlation coefficients of Bitcoin, Litecoin, Ethereum, and Ripple with the Chinese financial market are -0.0138, −0.0225, −0.0114, and −0.0143, which are negatively correlated. There is a correlation between cryptocurrencies and the impact of market volatility.

Emomotimi Agama

No abstract is available for this record.

Maslinda Mohd Nadzir, Rabea Abdulrahman Raweh, Hapini Awang, Huda Ibrahim

Without the requirement for third-party approval, cryptocurrency enables anonymous, secure, quick, and inexpensive financial transactions. Although cryptocurrency is gaining global popularity, its applications are still limited. This research aims to investigate the factors influencing the acceptance of cryptocurrency as an investment tool, focusing on the moderating role of government policy. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) extended with awareness, security, and trust, a survey was conducted with 220 respondents. Structural Equation Modelling (SEM) was employed to analyse the data. The findings revealed that the usage of cryptocurrencies is significantly affected by performance expectancy, facilitating conditions, social influence, awareness, and security in investment. However, trust does not affect the acceptance of cryptocurrency as an investment. The outcomes generate vital insights and strategies for cryptocurrency users, offering a crucial examination for stakeholders and professionals keen on understanding the underlying dynamics of cryptocurrency acceptance in investment.

Shahzad Ahmad, Zeeshan Iqbal, Imad Yousif Ahmad

This study analyses the growing importance of cryptocurrencies in Baluchistan, Pakistan, using the Rabby Wallet and Dex Screener to identify suspicious transactions linked to the Baluchistan Youth Council (BYC) in 2023.Baluchistan, one of Pakistan's least digitally connected areas, has adopted Decentralized Finance (DeFi) techniques, likely due to financial exclusion, surveillance avoidance, and informal remittance networks.The mixed-methods study analyses secondary data, tracks blockchain transactions, and reviews policy.Digital finance has structural constraints due to broadband penetration differences (15% in Baluchistan vs. 58.4% overall).Local traders, activists, and remittance beneficiaries may selectively adopt Rabby Wallet, according to wallet-level examinations.Event-window examination of Dex Screener data shows anomalous trading volumes, especially in low-liquidity tokens, amid BYC rallies and political mobilizations.These inconsistencies undermine cryptocurrency's significance in socio-political movements and its absorption into Baluchistan's shadow financial environment.The paper interprets these data using financial repression, technological adoption, and conflict economics.It contends that crypto adoption in Baluchistan is low but strategic in political finance and informal cross-border trade.The paper suggests improving financial inclusion, regulating decentralized platforms, and training investigators.This study illuminates how digital finance affects political movements in fragile regions and the risks and potential of bitcoin adoption in Baluchistan.

Chen Sun, Nurshazwani Muhamad Mahfuz, WANG YALI

No abstract is available for this record.

Ahmed Alrehaili, Martin White, Natalia Beloff

Abstract Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain‐based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain‐based cryptocurrency as a payment method in Saudi Arabia (BCAP‐SA) model. Factors within the model are assessed using an experimental vignette‐task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP‐SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region.

Samuel Sambasivam

Aim/Purpose To explore the potential of Federated Machine Learning (FML) in developing predictive models while ensuring data privacy and security. Background The rise of data-driven technologies has led to an increased focus on privacy concerns associated with centralized data storage. FML offers a decentralized approach, allowing organizations to collaboratively train models without sharing sensitive data (McMahan et al., 2017). Methodology This study employs a FML framework, utilizing local model training on decentralized datasets, followed by aggregation of model updates to create a global model. Privacy-preserving techniques, such as differential privacy, are also implemented (Dwork & Roth, 2014). Contribution This research contributes to the field of machine learning by demonstrating the efficacy of FML in predictive modeling, highlighting its potential for secure and privacy-conscious applications. Findings The study indicates that FML can effectively enhance model performance while maintaining the privacy of individual data sources. Recommendations for Practitioners Practitioners are encouraged to adopt FML techniques in applications requiring high data security, particularly in sectors such as healthcare and finance. Recommendation for Researchers Future research should explore advanced aggregation methods and evaluate the scalability of FML in diverse settings. Impact on Society The findings of this research have implications for the broader application of machine learning in sensitive areas, promoting data privacy while harnessing the power of collaborative intelligence. Future Research Further investigations should focus on the robustness of FML against adversarial attacks and its applicability in real-world scenarios.

Santhosh Chitraju

No abstract is available for this record.