Phishing attacks pose significant risks to the Ethereum ecosystem, comprising over 50% of Ethereum-related cybercrimes, leading to the emergence of many machine learningbased defenses.This paper introduces a comprehensive framework aimed at enhancing machine learning-based phishing detection in Ethereum transactions.The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and algorithm optimization.By systematically evaluating the strengths and limitations of existing approaches, we highlight gaps in current practices, particularly in feature manipulation and unsustainable performance outcomes.Through both analytical and experimental assessments, we demonstrate the framework's ability to streamline detection techniques, improving generalization and model effectiveness.Our findings emphasize the importance of refining detection strategies to meet the evolving challenges posed by sophisticated phishing schemes in the blockchain space.
O presente artigo investiga a otimização do throughput em redes blockchain baseadas em mecanismos de consenso Proof-of-Stake (PoS) a partir de um estudo comparativo de latência entre diferentes arquiteturas, parâmetros de configuração e arranjos de infraestrutura de rede. Partindo do desafio clássico da engenharia de sistemas distribuídos de conciliar escalabilidade, segurança e descentralização – conhecido popularmente como trilema do blockchain –, analisa-se como decisões de projeto, dentre as quais se incluem o desenho do protocolo de consenso, a duração dos intervalos (slots), o tamanho dos blocos, o número de validadores ativos e a topologia da rede P2P, afetam simultaneamente o volume de transações processadas por unidade de tempo e o tempo de confirmação percebido pelos usuários finais. A metodologia adotada combina revisão sistemática da literatura sobre modelos de desempenho em sistemas distribuídos e blockchains PoS, comparação de estudos empíricos que mensuram throughput e latência em plataformas públicas de referência – particularmente Ethereum pós-Merge, Solana e Polkadot – e análise conceitual dos trade-offs identificados. Os resultados obtidos sugerem que técnicas de otimização orientadas a parâmetros, tais como ajuste fino de tempos de slot e políticas de propagação acelerada de blocos, combinadas a inovações em camada de consenso – dentre as quais se destacam mecanismos híbridos com Proof of History e execução paralela de transações –, podem reduzir significativamente a latência de confirmação sem necessariamente comprometer a segurança criptográfica ou a robustez da rede. Entretanto, emergem limites estruturais associados à variabilidade de condições de rede em escala global, à heterogeneidade de capacidade computacional entre nós validadores e às exigências clássicas de tolerância a falhas bizantinas, os quais impõem fronteiras práticas ao ganho de throughput em ambientes permissionless. Conclui-se que uma abordagem sistêmica, que integre ajuste criterioso de parâmetros, desenho criterioso da infraestrutura de nós e mecanismos de monitoramento e benchmarking contínuos, é fundamental para o amadurecimento da infraestrutura Web3 e para o suporte a aplicações com requisitos estritos de qualidade de serviço, entre as quais se incluem protocolos de finanças descentralizadas, mercados de capitais tokenizados e aplicações sensíveis ao tempo.
A infraestrutura das redes de registro distribuído (DLT) atravessa uma fase de escrutínio rigoroso quanto à sua viabilidade ambiental e eficiência operacional. Este relatório técnico analisa exaustivamente os três principais paradigmas de consenso contemporâneos: Proof of Work (PoW), Proof of Stake (PoS) e Proof of History (PoH), sob a ótica do custo-benefício energético e da segurança sistêmica. O Proof of Work, embora detentor de uma robustez histórica inigualável, apresenta um consumo elétrico de proporções nacionais, demandando cerca de 1.375 kWh por transação na rede Bitcoin. O Proof of Stake, consolidado pela transição do Ethereum, reduziu o dispêndio energético em 99,95%, operando com uma média de 0,0026 kWh por transação através da substituição da exaustão computacional pelo compromisso de capital. O Proof of History, atuando como um relógio criptográfico integrado ao PoS na rede Solana, otimiza a ordenação temporal e a escalabilidade, resultando em um consumo marginal de 0,00051 kWh por transação, o mais eficiente entre os protocolos de alta performance. O estudo conclui que a migração para modelos de baixo consumo e alta vazão (throughput) é impulsionada não apenas por avanços técnicos, mas por marcos regulatórios como o MiCA da União Europeia, que exige transparência absoluta sobre o impacto climático dos ativos digitais.<br>
Tato práce se zabývá integrací protokolu zkLogin do aplikace Web3, aby se uživatel mohl přihlásit pomocí účtu OpenID Connect místo správy seed phrase. Práce vysvětluje princip zkLogin, porovnává jej s jinými přístupy k autentizaci ve Web3 a implementuje prototyp pro Sui a Ethereum. Větev pro Sui používá nativní podporu zkLogin, zatímco větev pro Ethereum používá Groth16 důkaz, chytrý účet, registr JWK a ERC-4337. Jednoduchý lending scénář ověřuje opakované změny on-chain stavu po přihlášení. Výsledky ukazují, že zkLogin může zjednodušit onboarding a omezit přímé zveřejnění vazby mezi účtem Web2 a on-chain adresou, ale prototyp stále závisí na poskytovateli identity, salt service, proving infrastruktuře a správě veřejných klíčů.
Khadija Sarwar, Syed Abid Ali Shah, Umar Ayaz Khan, Nosharwan Javied · 6 authors
Public and private sector tendering has traditionally relied on manual, paper-based, or loosely digitized workflows that are slow, opaque, and vulnerable to favouritism, record tampering, and unauthorized disclosure of bid information. Conventional e-tendering portals, although faster than paper-based processes, remain centralized, exposing them to a single point of failure and not guaranteeing that evaluation outcomes are auditable by bidders themselves. This paper presents the design and implementation of a Blockchain-Based Secure Tender Management System that uses Ethereum smart contracts, the Inter Planetary File System for decentralized document storage, and MetaMask-based identity/wallet management to automate tender publication, bid submission, evaluation, and contract award. The system is organized around two core smart contracts — a Listings Contract that manages tender creation, publication, and bidder communication, and an Agreement Producer Contract that manages award, billing, and payment logic — deployed and tested using Truffle and Ganache. By anchoring every transaction to an immutable, cryptographically verifiable ledger, the proposed framework removes intermediaries, enforces evaluation rules programmatically, and gives every stakeholder a synchronized, tamper-evident view of the tendering lifecycle. A review of blockchain-enabled procurement, construction-industry payment automation, and decentralized storage literature from 2020–2026 is used to contextualize the design choices and to identify open research directions, including scalability, gas-cost optimization, regulatory compliance, and integration with artificial intelligence for bid evaluation.
Дмитро Люшенко, Нодарі Горгіладзе, Олександр Туголуков, Михайло Шептун · 6 authors
Висока волатильність криптовалют та швидке поширення технологій штучного інтелекту (ШІ) у фінансовому секторі визначають необхідність точного прогнозування ризиків і поведінки інвесторів у процесі цифрової трансформації фінансових ринків. Метою дослідження є розробка системи економетричних моделей для оцінки прибутковості, волатильності, ліквідності та ризику падіння основних криптоактивів із використанням методів на основі ШІ. Методологічна структура включає моделі специфікацій ARDL-MIDAS, GARCH-MIDAS, PMG та logit, які поєднують високочастотні ринкові дані, макроекономічні індикатори, он-чейнгові метрики та індекси настроїв інвесторів. Вибірка охоплює вторинні дані за 2018-2025 роки для п'яти провідних активів — Bitcoin, Ethereum, BNB, XRP та Solana. Результати моделі ARDL-MIDAS показали, що збільшення обсягів торгівлі на 1% збільшує короткострокову прибутковість на 0,012 пункту, водночас зростання індексу VIX зменшує їх на 0,014 пункту. У моделі GARCH-MIDAS коефіцієнти α=0.085 та β=0.900 підтверджували високу інерцію волатильності біткоїна, а компонент MIDAS у VIX мав значний вплив 0.27. Модель панелі PMG виявила негативний довгостроковий вплив волатильності на ліквідність (−0,27) і позитивний ефект надпливу стейблкоїнів (−0,12), що вказує на функцію стабілізації. Логіт-модель довела, що збільшення на стандартне відхилення індексу VIX збільшує ризик краху на 52%. Отримані результати підтверджують ефективність поєднання економетричних методів і ШІ для аналізу цифрових фінансових ринків і технологій ШІ для аналізу цифрових фінансових ринків. Висновки підкреслюють можливість практичного застосування запропонованих моделей у фінансовому прогнозуванні, управлінні ризиками та політиці стабілізації цифрових активів у контексті розробки інтелектуальних фінансових систем на основі ШІ.
As the digital transformation accelerates, the security of cloud-based data storage and transmission has become a critical concern. Traditional cryptographic models often fail to ensure data integrity, privacy, and non-repudiation in distributed environments. This study proposes a blockchain-based security framework that integrates smart contracts, hash-based consensus, and distributed ledger technology (DLT) to enhance cloud data protection. The framework leverages immutable storage for audit trails, consensus validation for tamper detection, and homomorphic encryption for privacy-preserving computations. Experiments using Hyperledger Fabric and Ethereum private testbeds reveal that blockchain integration improves data integrity verification efficiency by 42% and reduces unauthorized modification risks by 35% compared to conventional systems. The findings underscore blockchain’s role as a cornerstone of trustworthy cloud architectures, ensuring both transparency and confidentiality in global digital ecosystems..
Although many academic studies have examined volatility spillovers and dynamic correlations between stock markets, they have largely overlooked the perspective of Small and Medium-Sized Enterprise (SME) markets. On this basis, this study explores the interconnectedness and volatility correlation between Decentralized Finance (DeFi) markets and SME markets. To understand the correlation between these markets, we empirically analyse six European SME market indices—the FTSE AIM All-Share Index (AIM), BIST SME Industrial Index (BISTSME), Euronext Growth All-Share Index (EURONEXT), First North All-Share Index (FIRSTNORTH), IBEX Medium Cap Index (IBEXC), and Scale All-Share Performance Index (SCALE)—alongside three cryptocurrencies: Aave (AAVE), Ethereum (ETH), and Uniswap (UNI); two stablecoins: Dai (DAI) and USD Coin (USDC); and one synthetic asset: Synthetix (SNX). The study employed BEKK-GARCH and DCC-GARCH to analyse the existence of spillover effects and correlations from October 5, 2020, to August 18, 2024. The findings indicate that AAVE, ETH, and UNI, in particular, transmit significant volatility to the EURONEXT and FIRSTNORTH markets. However, bidirectional volatility spillover was detected between EURONEXT and AAVE, ETH, UNI, USDC, and SNX, and FIRSTNORTH and AAVE, ETH, UNI, and SNX. This suggests volatility interdependence between these markets and the existence of potential risk contagion channels.
The thriving Non-Fungible Token(NFT) market, despite its innovative redefinition of digital ownership, faces malicious attacks and challenges, notably from widespread wash trading. In this paper, we examine the underexplored relationship between NFT rarity and wash trading.We present a novel approach to NFT market dynamics, by establishing the first comprehensive formal framework for NFT trait systems, including essential definitions, a robust taxonomy, precise rarity calculation, and verifiable properties. Building upon this, we conduct an empirical analysis of NFT rarity and wash trading across 30 collections including 336,133 NFTs, 764,679 transactions, total volume of 14,394,949 in Ethereum. Our findings reveal the strong inverse correlation between a collection’s price-rarity coefficient and its wash trading volume, showing that the top 5 collections with the lowest price-rarity coefficients are overwhelmingly dominated by wash trading, averaging 89.29% of their total volume, in contrast to the vast majority of other collections which exhibited negligible volumes, consistently below 2%. Case studies further highlight the power of this price-rarity analysis as a novel anomaly detection tool: it exposed wash trading-induced distortions in CryptoPunks by detecting an outlier previously undetectable by existing graph-based approaches. We also demonstrate Rektguy’s remarkable resilience—absorbing 20.04% wash volume possibly due to strong rarity-price correlation. Our findings establish rarity as an intrinsic resilience factor against manipulation, fundamentally reshaping approaches to NFT market analysis and robust anomaly detection.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Advanced Steganography and Watermarking Techniques
As stablecoins become increasingly prevalent in financial crimes, their usage for illicit activities has reached a scale of USD 51.3 billion. Detecting phishing activities within stablecoin transactions has emerged as a critical challenge in blockchain security. Currently, existing detection methods predominantly target mainstream cryptocurrencies like Ethereum and lack specialized models tailored to the unique transaction patterns of stablecoin networks. This paper introduces a deep learning framework, BERTSC, based on multi-modal fusion. The model integrates three core modules graph convolutional networks (GCNs), BERT semantic encoders, and soft prompt encoders to identify malicious accounts. The GCN constructs directed multi-graph representations of account interactions, incorporating multi-dimensional edge features; the BERT encoder transforms discrete transaction attributes into semantically rich continuous vector representations; the soft prompt encoder maps account interaction features into learnable prompt vectors. An innovative three-way gated dynamic fusion mechanism optimally combines the information from these sources. The fused features are then classified to predict phishing account labels, facilitating the detection of phishing scams in stablecoin transaction datasets. Experimental results on large-scale stablecoin datasets demonstrate that BERTSC outperforms baseline models, achieving improvements of 4.96%, 3.60%, and 4.23% in Precision, Recall, and F1-score, respectively. Ablation studies validate the effectiveness of each module and confirm the necessity and superiority of the three-way gating fusion mechanism. This research offers a novel technical approach for phishing detection within blockchain stablecoin ecosystems.
Dr. Megala Rajendran, R. Gopalakrishnan, Dr.A. Dharmaraj, Dadajon Dadabayev Rustamovich
Background: Quantum computing poses a threat to classical signatures, like ECDSA, and makes long-lived blockchain smart contracts, particularly those used in a system of the circular economy and sustainability, susceptible to future forgery and governance attacks. Abstract: This paper presents a quantum resilient smart contract lattice architecture to achieve ethical governance and resource tracking in the use of a circular economy and maintain realistic performance. Methods: The architecture uses a NIST‑track lattice‑based post‑quantum signature scheme (CRYSTALS‑Dilithium) with one signature per transaction in an Ethereum‑like environment, adds batched post‑quantum verification opcodes to the virtual machine and a post‑quantum‑aware gas model, and introduces Solidity contracts for recycle passports, tokenized waste‑management incentives, and DAO‑based governance. Ethical governance is operationalized using a transparency index together with quantitative fairness and inclusiveness measures derived from reward distributions and participation rates. Results: The proposed framework has a 2.0 ms verification latency, 450 transactions per block, 70% relative throughput, and 130 GB/year storage, compared to 2.8 ms verification, 268 transactions per block, 55% relative throughput, and 140 GB/year storage in the hybrid post‑quantum baseline and the classical ECDSA configuration. Conclusion: These findings suggest quantum resilient smart contracts are a promising basis of long-horizon circular economy governance, which provides superior security and ethics by design assurances and sustains competitive performance and sustainability attributes compared to both classical and hybrid post-quantum baselines.
The state-of-the-art review comprehensively examines access control mechanisms for securing cloud computing environments, emphasizing their architectural evolution and performance efficiency. Conventional access control models such as Role Based Access Control (RBAC) and Attribute Based Encryption (ABE), though widely adopted, continue to face limitations including single points of failure, centralized policy management, and limited transparency in audit trails. Recent studies report average encryption and decryption times below one second in conventional schemes, yet these models struggle with scalability and dynamic revocation in distributed settings. The integration of blockchain technology addresses many of these challenges through its decentralized, immutable, and transparent infrastructure. Blockchain based access control frameworks implemented on platforms such as Hyperledger Fabric and Ethereum leverage smart contracts to automate policy enforcement and achieve throughput gains of up to 42 percent with transaction latencies near 39 milliseconds. By distributing trust and enabling verifiable audit trails, these models enhance data integrity, accountability, and compliance. This survey consolidates and analyzes current research in both conventional and blockchain based access control for cloud and IoT ecosystems, identifying performance tradeoffs, regulatory considerations, and future research directions toward secure, transparent, and scalable access management.
Marwa Ali Hamdan AL-Jabri, Nafisa Abul Ghafoor Othman AL-Ansari
Access control is an important part of cybersecurity in distributed systems since conventional centralized mechanisms are not always sufficient. Due to blockchain, individuals have begun to employ decentralized access control models as they are capable of enhancing transparency, auditing and defending against fraud. At the reason of this report, we survey various blockchain-based access control systems, paying special attention to their architectures, confirmation mechanisms, identity models and policy enforcement mechanisms. We categorize the current literature into various groups based on their platforms (e.g. Ethereum, Hyperledger, Fabric), control mechanisms (e.g. RBAC, ABAC and capability-based) and whether they introduce additional privacy-tools such as zero-knowledge proofs and decentralized identifiers. The paper analyzes and describes the key gaps in current frameworks in terms of scalability, interoperability and computing expenses. Then, the shortcomings of the current research are pointed out so that they could guide future efforts in the field of blockchain-based access control systems.
Cryptocurrencies have upended the financial industry since they provide decentralized and peer-to-peer transactions. However, due to market volatility and the numerous non-linear relationships between price dynamics and human mood, forecasting Bitcoin values is a difficult task. The deep learning architecture shown in this work combines sentiment confidence scores derived from cryptocurrency-related tweets utilizing Transformer-based natural language processing with historical price indicators. The model incorporates Convolutional Neural Networks (CNN) to detect local time-series patterns and Long Short-Term Memory (LSTM) networks to produce long-term dependencies. We apply this architecture, involving sequence-based preprocessing and normalization, to Bitcoin and Ethereum to ensure robustness. Evaluations in comparison to baseline models Sentiment fusion dramatically increases predicting accuracy, especially during times of market turbulence, according to CNN-LSTM without sentiment, vanilla LSTM, and ARIMA. Our research helps develop scalable, sentiment-aware financial forecasting algorithms that better reflect the behavior of real markets.
Carlos Alberto Durigan, Fernando José Barbin Laurindo
Blockchain is a Distributed Ledger Technology (DLT) which supports cryptocurrencies, Decentralized finance (DeFi) is a blockchain-based financial infrastructure, the term generally refers to an open, permissionless, and highly interoperable protocol stack built on public smart contract platforms, such as the Ethereum blockchain. DeFi does not rely on intermediaries and centralized institutions. Instead, it is based on open protocols and decentralized applications (Dapps). Considering that there are many digital coins, stablecoins and recently the advent of central bank digital currencies (CBDCs by Central Banks) and tokenized assets it is important to observe that these protocols may interact among themselves. These IT protocols interactions may be complex and there should be effective IT governance frameworks to guide points like interoperability and interconvertibility of digital assets based on DLTs protocols. IT governance framework based on these technologies is still a challenge in the literature. Considering these points, this paper seeks to explore literature through a Systematic Literature Review methodology in order to find the state of the art about this theme. Results show that Literature explore DLT governance as a whole, including information technology (IT) aspects. However, there is a lack in the literature about IT governance for interoperability and interconvertibility among complex DLT protocols interactions. Discussions, future research, limiting factors and conclusions are fully stated. Keywords: Distributed Ledger Technology (DLT), IT Governance, Central Bank Digital Currency (CBDC), Tokenization, Decentralized Finance (DeFi).
This study systematically reviews scientific research on predicting cryptocurrency markets. A total of 790 articles obtained from the Web of Science database were included in the analysis, and the structure of the literature was evaluated using bibliometric methods. The preliminary investigation indicated that studies examining the prediction of cryptocurrencies have undergone a substantial increase since 2016. While a significant proportion of the extant literature pertains to Bitcoin, the first cryptocurrency, it is evident that other cryptocurrencies, such as Ethereum, have also attracted the attention of researchers over the years. The analysis yielded four primary categories: machine learning-based prediction methods, financial risk and volatility analyses, behavioral and technical determinants, and finally, advanced deep learning methods. In the context of cryptocurrency prediction, studies have underscored the significance of attributes, emphasizing their role in enhancing the efficacy of prediction models. These studies have also highlighted the impact of integrating machine learning and deep learning-based models with conventional methods in enhancing the performance of established models. The study emphasizes the necessity to direct future research towards the integration of behavioral indicators and the examination of multiple market relationships.
Dockerised blockchain solution can mitigate the low levels of distributed technology adoption in small and medium enterprises. It can be done via designing and implementing an environment which inherits ease of deployment and scalability of containerized systems with safety and transparency of distributed applications. Practical implementation of a dockerized blockchain solution designed as a demonstrative implementation for existing client–server architecture is described in this paper. This solution uses Docker containers to simplify the setup and deployment of a private blockchain network, a mediator server and a reverse proxy. Implementation of this system on a low scale demonstrates feasibility of integrating blockchain technology into existing business processes without fundamental architectural changes and acknowledges deployment and maintaining challenges that usually accompany distributed systems using private blockchain. Discussed implementation is a demonstration of designed architecture being potentially a reproducible and easily maintainable environment for logging and validating data through an immutable ledger on a smaller scale. Proof of concept successfully validates the core idea. The implementation shows a mediator server intercepting client request, recording them on a private Ethereum blockchain via a JSON-RPC interface, and then forwarding them to the original server. This confirms the solution’s ability to introduce a trusted, intermediate layer for data immutability. The project demonstrates a working framework for embedding distributed ledger technologies into client–server ecosystems. While the current Proof of Work consensus mechanism presents scalability limitations, the architecture provides a strong foundation for future research, including migrating to more efficient consensus mechanisms and integrating smart contracts.
Dockerised blockchain solution can mitigate the low levels of distributed technology adoption in small and medium enterprises. It can be done via designing and implementing an environment which inherits ease of deployment and scalability of containerized systems with safety and transparency of distributed applications. Practical implementation of a dockerized blockchain solution designed as a demonstrative implementation for existing client–server architecture is described in this paper. This solution uses Docker containers to simplify the setup and deployment of a private blockchain network, a mediator server and a reverse proxy. Implementation of this system on a low scale demonstrates feasibility of integrating blockchain technology into existing business processes without fundamental architectural changes and acknowledges deployment and maintaining challenges that usually accompany distributed systems using private blockchain. Discussed implementation is a demonstration of designed architecture being potentially a reproducible and easily maintainable environment for logging and validating data through an immutable ledger on a smaller scale. Proof of concept successfully validates the core idea. The implementation shows a mediator server intercepting client request, recording them on a private Ethereum blockchain via a JSON-RPC interface, and then forwarding them to the original server. This confirms the solution’s ability to introduce a trusted, intermediate layer for data immutability. The project demonstrates a working framework for embedding distributed ledger technologies into client–server ecosystems. While the current Proof of Work consensus mechanism presents scalability limitations, the architecture provides a strong foundation for future research, including migrating to more efficient consensus mechanisms and integrating smart contracts.
Voting is a cornerstone of democracy, enabling individuals to choose their leaders and influence decisions shaping their communities and future. Traditional voting systems, however, face numerous challenges, such as long queues, paper-based inefficiencies, and security vulnerabilities. To address these issues, blockchain technology has emerged as a transformative solution, leveraging its decentralized and secure infrastructure. This paper presents a blockchain-based e-voting system aimed at enhancing accessibility, security, and efficiency. By utilizing the Ethereum blockchain and smart contracts, the system ensures transparency, immutability, and tamper-proof vote recording. Furthermore, the integration of AI-powered facial recognition technology reinforces identity verification, guaranteeing that only authorized voters participate. Comprehensive testing, including simulations and stress analyses, confirms that the proposed model enhances the voting process by offering a secure, user-friendly, and reliable digital platform. This research highlights the potential of combining blockchain and AI to modernize voting systems, fostering trust and inclusivity in democratic processes. This paper introduces a blockchain-based e-voting system that simplifies the voting process while ensuring maximum security and trust. By using the Ethereum blockchain and smart contracts, votes are recorded and verified securely, leaving no room for manipulation. To make the system even more robust, AI-powered facial recognition is integrated to confirm voter identity, ensuring only eligible individuals can participate. The system has been tested extensively to ensure it’s not only secure but also easy to use, providing a seamless experience for voters. This combination of blockchain and AI has the potential to revolutionize voting, making it fairer, more inclusive, and efficient for everyone.
This study aims to analyze the volatility spillovers between Bitcoin and Ethereum, the two main actors in the cryptocurrency market, and altcoins across sectoral and financial groups. Using data from January 1, 2021, to March 6, 2023, the study applied the VAR-based method developed by Diebold and Yılmaz (2012) and measured both directional and total volatility spillovers. The findings show that Bitcoin's volatility largely stems from internal dynamics and spreads to other cryptocurrencies to a limited extent. In contrast, Ethereum is more affected by external shocks and exhibits a stronger volatility spillover across the market. Among altcoin categories, Gaming, Analytics, and DeFi groups were found to be the most influential in volatility transmission, while thematic tokens such as NFT, Web3, and Metaverse were more sensitive to external volatility. In contrast, stablecoins and tokens in the identity and healthcare sectors were found to have relatively low volatility and a more stable structure. These results offer important insights for investors and regulators regarding risk management strategies and portfolio diversification. The study provides a valuable framework for understanding the systematic volatility dynamics within the cryptocurrency ecosystem
We present a game semantics framework for open-world safety analysis of Ethereum smart contracts. We model the interaction between a contract and its environment as a two-player game between the contract and the environment, and prove up to gas model approximations soundness: every assertion violation found corresponds to a real execution; and completeness: every open-world execution is captured. To our knowledge, this provides the first formal open-world interaction semantics for Ethereum smart contracts with mathematical guarantees of soundness and completeness. We implement this framework in YulTracer, an assertion reachability tool for real-world Solidity contracts, built on Yul, the intermediate language of the Solidity compiler. YulTracer uses concrete execution and exhaustively explores game traces within user-specified bounds. We evaluate it on reentrancy benchmarks, where YulTracer achieves 100% recall and precision -- the only tool to do so from those we examined -- and on two large real-world exploits (the DAO and PredyPool), where it detects the known vulnerabilities and produces no false positives on fixed versions. To our knowledge, YulTracer is the first tool to achieve this level of precision on real-world contracts without false positives. We additionally demonstrate generality of the approach via the examination of access control benchmarks.
ABSTRACT This study investigates the impact of environmental attention on cryptocurrency market volatility by introducing the Crypto Environmental Attention Index (CEAI), a new metric inspired by Wang et al. (2022) and constructed using daily web search data. Environmental concerns can significantly impact the popularity and volatility of cryptocurrencies, influencing risk perceptions, and shaping market dynamics. Using vector autoregression (VAR), vector error correction models (VECM), and Granger causality tests on data from 2014 to 2022, the study finds that Ethereum's volatility is strongly influenced by the CEAI in both the short and long‐term, whereas Bitcoin volatility has a short‐term unidirectional effect on environmental attention and a bidirectional relationship in the long term. This study is situated within a broader economic framework of sustainable finance, the transition to greener blockchain technologies, and regulatory responses to environmental issues. It offers actionable insights for risk management, policy formulation, and cryptocurrency valuation using environmental, social, and governance (ESG) criteria.