Autonomous platforms for fintech, decentralized finance, and digital civil infrastructures are at the research frontier. Delivering on their promise requires a foundational approach. Future research and development directions are organised by core architectural principles, enabling technologies, major challenges and risks, methods for development and evaluation, and governance models. Autonomous economic interaction and decision-making are principally guided by policy goals. Independence from human involvement cannot be guaranteed, especially when external agents fulfil custodial roles, but risk can be mitigated by solidifying the foundations. The term “autonomous platform” constitutes a composite of economic theory and systems design. Platforms support economic interactions enabled by information and communication technology—in particular, the Internet. Their distinctive feature is an architecture composed of services provided by multiple stakeholders. Platform engineering is a design discipline that seeks to deliver the hoped-for benefits, including lower costs, greater selection, and novel business models, while mitigating risks such as fraud and the abuse of market power. The promise of autonomy stems from the deployment of becoming-type, human-compliant purpose design in an effective oversized-modular architecture and begins with the fulfilment of core architectural principles—an autonomous, modular, and composable layer for economic interaction and decision-making.
Cryptocurrencies have started gaining ground as investment vehicles. Cryptocurrencies exhibit characteristics that differentiate them from traditional financial assets. In 2009, Bitcoin (BTC), the first digital currency, was launched. In 2021, the Securities and Exchange Commission (SEC) approved ProShares Bitcoin Strategy (BITO), the first U.S. Bitcoin futures exchange-traded fund (ETF). In 2024, SEC gave final approval for spot Ether (ETH) ETFs to start trading, further legitimizing the asset class. Although cryptocurrencies share many features of alternative assets, they are hindered by high volatility and regulatory uncertainties. Extant literature studies cryptocurrencies as alternative investments from various perspectives. Using market data, this empirical paper aims to contribute to the literature by studying the extent to which cryptocurrencies improve the risk-return profile of a diversified portfolio. Specifically, we do so by examining the economic impact of including Bitcoin for a passive investor investing in the U.S. Stock market index (S&P 500 index).
Nourhaine Nefzi, A. Melki, Sahar Loukil, Ahmed Jeribi
Abstract This study investigates the dynamic connectedness within the cryptocurrency market by analyzing four distinct cryptomarket blocks: Bitcoin and Ethereum (conventional cryptocurrencies); PAXG, DGX, and GLC (gold-backed cryptocurrencies); LINK and MNK (decentralized finance); and THETA and MANA (nonfungible tokens). Using the time-varying parameter quantile vector autoregressive (TVP-Quantile VAR) model for the period 2019–2023, our analysis reveals significant insights into the risk transmission dynamics among cryptocurrencies. Both conventional cryptocurrencies exhibit a consistent net transmitter effect in extreme periods, whereas decentralized finance (DeFi) and nonfungible tokens (NFTs) shift between a net shock transmitter and a net shock receiver over time and quantiles. Moreover, our results shed light on the hedging and safe haven properties of these assets. By linking the dynamic connectedness findings with established literature on hedging and safe haven functions, we elucidate how these cryptocurrencies perform under varying market conditions. Specifically, we report that the role of LINK, MNK, THETA, and MANA as reliable safe-haven assets is contingent upon the observed period. We also observe the hedge and safe haven properties of selected gold-backed cryptocurrencies within the network. Overall, our findings suggest that, despite the dynamic connectedness of the cryptocurrency market, investors have the flexibility to diversify across these digital assets.
This paper presents the implementation oriented development of an interactive web platform designed to bring transparency and trust to charitable giving through the use of blockchain technology. This DApp integrates both Web2 and Web3 components: Here organizations create verified charity campaigns so that donors contribute directly through Meta-Mask a cryptocurrency wallet, with all transactions immutably recorded on the blockchain for public auditability. The backend (Web2) manages user data, campaign verification, and document storage, enforcing legitimacy through decentralized storage (IPFS). Also with the use of web2 has helped to create a more user friendly and attractive user interface layouts. Etherium Smart contracts are used to handle and release donations based on predefined conditions. A Merkle Tree algorithm is implemented to provide cryptographic proof for inclusion of donations in charities. This platform solves common challenges of traditional charity systems, such as mismanagement, high intermediary fees, and mainly donor mistrust, by offering a secure, decentralized, and automated donation ecosystem.
This article examines the revolutionary impact of Artificial Intelligence (AI) on transforming cryptocurrency trading, a sector characterised by extreme volatility, dynamism, and nonlinear data. Through a rigorous bibliometric analysis based on the Web of Science database, this study examines a sample of 555 scientific papers published between 2016 and 2025, utilising the PRISMA protocol for systematic selection, and tools such as VOSviewer and MS Excel. The analysis identifies five major thematic clusters: (1) blockchain infrastructure and AI integration in decentralised ecosystems, (2) data analysis and practical applicability in crypto markets, (3) financial and social data analysis—machine learning algorithms, (4) algorithmic trading and automation, and (5) prediction and modelling of crypto market developments. The originality of this study lies in providing an overview of the implementation stage of these technologies by integrating the results into a map of Technology Readiness Levels (TRLs). The findings highlight a clear transition from traditional statistical methods to autonomous decision-making systems capable of processing massive volumes of data for portfolio optimisation. This study’s limitation is that it may require periodic updates, as the AI and cryptocurrency landscape are constantly evolving.
Unlike Ethereum, which was conceived as a general-purpose smart-contract platform, Bitcoin was designed primarily as a transaction ledger for its native currency, which limits programmability for conditional applications. This constraint is particularly evident when considering oracles, mechanisms that enable Bitcoin contracts to depend on exogenous events. This paper investigates whether new oracle designs have emerged for Bitcoin Layer 1 since the 2015 transition to the Ethereum smart contracts era and whether subsequent Bitcoin improvement proposals have expanded oracles' implementability. Using Scopus and Web of Science searches, complemented by Google Scholar to capture protocol proposals, we observe that the indexed academic coverage remains limited, and many contributions circulate outside journal venues. Within the retrieved corpus, the main post-2015 shift is from multisig-style, which envisioned oracles as co-signers, toward attestation-based designs, mainly represented by Discreet Log Contracts (DLCs), which show stronger Bitcoin community compliance, tool support, and evidence of practical implementations in real-world scenarios such as betting and prediction-market mechanisms.
Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele · 31 authors
Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.
This working paper introduces the Blockchain First-Principles Analysis (BFPA) framework, a novel methodology for evaluating distributed ledger systems by constructing explicit derivation chains from physical laws and cryptographic assumptions through a praxeological action axiom to concrete protocol design decisions. The framework features a four-level axiom hierarchy (physics, cryptography, praxeology, social consensus), a Nash equilibrium gate for social layer stability, a four-stage stability profile, a lock-in typology distinguishing design-emergent, ecosystem-emergent, corporate-imposed, and regulatory-granted lock-in, and a network effect genesis model identifying five necessary conditions for spontaneous adoption. Systematic application to eight major blockchain systems (Bitcoin, Ethereum, Solana, Monero, XRP, Polkadot, Tezos, BNB Chain) reveals that epistemic design quality correlates weakly with market outcomes, while lock-in type and network effect genesis conditions are substantially stronger predictors. The analysis provides principled explanations for the Tezos Paradox and the Monero Paradox. Comments welcome.
With the rapid expansion of the global cryptocurrency market since the 2018 Bitcoin investment boom, the number of cryptocurrency users has increased significantly. Despite the emergence of various cryptocurrency wallets, issues such as users' inability to properly manage their assets and the growing number of security incidents and crimes continue to undermine trust in digital asset storage. In this context, there is an urgent need for a system that can ensure secure asset protection and address users' anxiety regarding cryptocurrency management. However, the concept of cryptocurrency security remains undefined, relevant laws and regulations are not yet institutionalized, and academic research in this area is still limited. To address these challenges, this paper proposes an Ethereumbased cryptocurrency wallet system that not only enhances the functional and security aspects of existing wallets but also enables cryptocurrency delegation and ownership transfer. We designed and implemented a DApp that allows users to securely store, delegate, and transfer cryptocurrencies through an escrow account implemented via smart contracts on the Ethereum blockchain. By presenting a practical implementation of a transferable and delegatable wallet, this study contributes to improving user trust and asset safety, while laying the foundation for legal and technological innovation in the digital asset ecosystem.
Research background and purpose Digital technologies offer tangible economic benefits but are also exposed to the risk of misuse. Crowdfunding is a special support form for business, cultural or social enterprises. Due to anonymity, fragmentation of capital and wide coverage, crowdfunding transactions are particularly vulnerable to the risk of criminal activities related to the concealment of the source of income or illegal changes of the financing objective. This article addresses the risks of money laundering and terrorism financing, particularly on the specifics of crowdfunding. Research has proposed a synthetic risk indicator for AML/CFT, which may measure the level of risk and vulnerability of crowdfunding to money laundering and terrorism financing. Design/methodology/approach The discussion in the article is presented against the background of a comprehensive and integrated review of literature, covering national and foreign sources. The theoretical part of the article utilizes: method of analysis and criticism of literature, analysis and synthesis, and method of analysis and logical construction. In the empirical part, to assess the level of risk and vulnerability of crowdfunding to AML/CFT risk compared to other areas, a research procedure based on the TOPSIS linear ordering method was used. The analysis covers the years 2019 and 2023. Findings The results of the studies show that crowdfunding is one of the most vulnerable areas at risk of money laundering and terrorism financing. The high position in the ranking in 2019 and 2023 resulted mainly from the dynamic development of the crowdfunding market in Poland, its increasing availability, a high degree of decentralization, the occurrence of cross-border transactions and the increasing diversity of platforms in their business model. Maintaining the benefits of crowdfunding requires the simultaneous implementation of effective remedies, increased campaign transparency and close cooperation with supervisory authorities and institutions combating financial crime. Value added and limitations The study makes an important contribution to the literature on the subject, providing information on the criminality of crowdfunding. The results of the study can be used by supervisory and regulatory authorities as a tool for shaping security in innovative segments of the financial system. The main limitation was the relatively small number of variables selected for the synthetic measure.
Amid the institutionalization wave of Decentralized Finance (DeFi), U.S. institutional Liquidity Providers (LPs) have emerged as the core incremental capital for leading Decentralized Exchanges (DEXs). However, the adaptation gap between Uniswap V4's concentrated liquidity mechanism and institutional risk preferences, as well as regulatory compliance requirements, has hindered their market entry. This study focuses on the integration of "technical characteristics - institutional constraints - precise pricing" and constructs a machine learning pricing model optimized across three dimensions: return, risk, and compliance. By integrating Uniswap V4 on-chain data, institutional risk preference data, and market data, a Stacking ensemble architecture combining LightGBM and CNN-LSTM is designed, incorporating 22 core features to achieve precise pricing. Empirical results show that the model's Mean Absolute Error (MAE) on the test set was reduced by 37% compared to the benchmark, and the Root Mean Square Error (RMSE) is reduced by 42%. The Sharpe ratio reaches 1.87 (an increase of 62% compared to the benchmark), with a volatility of 15.3% and a compliance adaptability score of 91. In the case study, a $150 million liquidity supply achieved a 19.7% annualized return and an 8.3% maximum drawdown, successfully passing SEC compliance review. This research fills the gap in institution-oriented pricing models for V4, improves the institutional extension of Automated Market Maker (AMM) pricing theory, and provides a risk-controllable and compliance-adaptable pricing tool for U.S. institutions participating in DeFi, promoting the transformation of the DeFi ecosystem towards standardization and institutionalization. By aligning the V4 Hook mechanism with U.S. regulatory frameworks, this research provides a scalable technical standard for institutional DeFi adoption, reinforcing the competitive advantage of the U.S. Web3 financial ecosystem.
this study rigorously scrutinizes the revolutionary impact of artificial intelligence (AI) on banking organizations within India. It definitively analyses the transformative effects of AI on the Indian financial industry by reviewing pertinent literature, compelling case studies, and empirical data. The paper first establishes the major ways. AI unequivocally alters the financial sector. It then details how Indian Banking institutions effectively deploy AI across critical areas such as customer service, algorithmic trading, risk management, fraud detection, credit scoring, and regulatory compliance. The integration of AI into India’s financial ecosystem is highlighted through examples from major banks, fintech companies, and regulatory agencies, showcasing the methods used and the outcomes achieved. Furthermore, this study explores the impacts and challenges associated with AI implementation in the Indian banking industry [14]. It delves into the cultural factors, current regulations, data availability, talent acquisition, and regulatory frameworks that shape the application of AI in Indian banks. The combination of Decentralized finance and AI offers a revolutionary partnership that might completely change the sector, increase its flexibility, and lay the foundation for long-term viability. In recent years, AI and Decentralized finance have become prominent advances in technology that have attracted a lot of interest and acceptance. In conclusion, this study comprehensively analyses AI's effects on India’s banking sector. This research paper is based on secondary data with the help of various journal and websites. Researcher paper benefits to many Policymakers, practitioners, and scholars will find invaluable insights contributing to the growing literature on technology-driven transformations. The recommendations provided will enable stakeholders to effectively harness AI’s capabilities while proactively addressing inherent risks and challenges, thereby enhancing the resilience, efficiency, and customer-centric focus of financial institutions in India and ensuring their competitiveness in an increasingly digital landscape. This research highlights the need to adopt a-worthy strategies for the prevention of active fraud, eventually contributes to the integrity of financial systems.
Open access
Innovations and Analysis in Business and Education
Omar M. Bawazeer, Md Mahfuzur Rahman, Mohammad Hammoudeh
Smart contracts (SC) are deployed on blockchain platforms without sufficient descriptive metadata. This lack of metadata significantly limits their discoverability, making it challenging for consumers to identify and select suitable contracts. This paper presents a comparative analysis of existing smart contract registry design approaches. It first identifies the core design properties of smart contract registries, such as descriptive metadata, description format, registry type, structure, platform scope, and discovery mechanism, and uses these dimensions as the foundation for our analysis. Drawing from this analysis, we propose a two-part taxonomy that distinguishes between purpose-based and business-based designs. We then analyze representative smart contract registry designs and highlight current key challenges in the field. By mapping the strengths, limitations, and trade-offs of these designs, this paper provides a reference to guide developers and platform architects. It aims to support the design of more efficient, transparent, interoperable, and decentralized smart contract registries, ultimately improving contract discoverability and selection for consumers.
Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thus, a shock to one token may result in significant and uncontrolled contagion effects. As the DeFi ecosystem becomes increasingly linked with traditional financial infrastructure through instruments, such as stablecoins, the risk posed by this dynamic demands more powerful quantification tools. We introduce DeXposure-FM, the first time-series, graph foundation model for measuring and forecasting inter-protocol credit exposure on DeFi networks, to the best of our knowledge. Employing a graph-tabular encoder, with pre-trained weight initialization, and multiple task-specific heads, DeXposure-FM is trained on the DeXposure dataset that has 43.7 million data entries, across 4,300+ protocols on 602 blockchains, covering 24,300+ unique tokens. The training is operationalized for credit-exposure forecasting, predicting the joint dynamics of (1) protocol-level flows, and (2) the topology and weights of credit-exposure links. The DeXposure-FM is empirically validated on two machine learning benchmarks; it consistently outperforms the state-of-the-art approaches, including a graph foundation model and temporal graph neural networks. DeXposure-FM further produces financial economics tools that support macroprudential monitoring and scenario-based DeFi stress testing, by enabling protocol-level systemic-importance scores, sector-level spillover and concentration measures via a forecast-then-measure pipeline. Empirical verification fully supports our financial economics tools. The model and code have been publicly available. Model: https://huggingface.co/EVIEHub/DeXposure-FM. Code: https://github.com/EVIEHub/DeXposure-FM.
The approval of spot Bitcoin and Ether ETFs in 2024 has ignited widespread interest in tokenization, with market potential estimated at $10 trillion. This shift is accelerating the adoption of decentralized finance (DeFi), crypto staking, artificial intelligence (AI)-driven payments, and private stablecoins. Innovations such as agentic AI and blockchain integration are transforming financial services, though they introduce systemic risks and cybersecurity challenges. U.S. federal and state policies are increasingly supportive of digital assets, including state-level Bitcoin reserves. Meanwhile, the global rise of CBDCs reflects governments’ intent to harness tokenization’s benefits while maintaining monetary control. Ensuring security, transparency, and governance remains critical.
Robyn McCormack, Pamela Kent, Richard Kent, Young K. Ro · 5 authors
Purpose The purpose of this study is to conduct a systematic literature review of non-fungible tokens (NFTs) within the business-related disciplines of finance, marketing, management, law, economics, accounting and entrepreneurship. Key research themes and directions for future research are identified. Design/methodology/approach A mixed-methods synthesis is employed, combining bibliometric mapping with qualitative thematic analysis to trace the development of NFT research across business disciplines from 2021 to 2024. Findings The most dominant theme across the disciplines is the underlying economic modeling and valuation explaining how NFTs grow and maintain value. Researchers question whether NFTs hold legitimacy as tradeable assets within traditional financial systems. The consumer behavior discipline covers another central idea that NFT adoption introduces additional complexity to established assumptions about digital ownership, identity expression and platform engagement. Other notable themes include hedging and safe haven roles, fraud and financial integrity, legal and intellectual property issues, blockchain infrastructure, innovation, arts and entertainment, taxation and fiscal policy, and review and conceptual work. These themes are covered across the disciplines with the highest number of papers in finance (57 papers), followed by marketing (42), management (17), law (14), accounting and economics (6 each) and entrepreneurship (5). Originality/value NFT research has largely been fragmented within individual disciplines. This study adds value by offering an integrated review across business domains using bibliometric mapping and thematic analysis.
Η παρούσα διδακτορική διατριβή διερευνά τον εξελισσόμενο ρόλο του Bitcoin στο διεθνές χρηματοοικονομικό σύστημα, εστιάζοντας τόσο στις δυναμικές αλληλεπιδράσεις του με τις παραδοσιακές αγορές, όσο και στους μακροοικονομικούς παράγοντες που καθορίζουν τη μεταβλητότητά του. Η διατριβή αποτελείται από τρία εμπειρικά δοκίμια. Στο πρώτο κεφάλαιο εφαρμόζεται η μεθοδολογία των Atasoy και Özkan (2024), η οποία επιτρέπει τον εντοπισμό περιόδων εντός του συνολικού δείγματος κατά τις οποίες εκδηλώνονται επεισόδια contagion μεταξύ των εξεταζόμενων μεταβλητών. Το προτεινόμενο πλαίσιο συνδυάζει το υπόδειγμα DCC-GARCH με χρονικά μεταβαλλόμενους ελέγχους αιτιότητας κατά Granger, προκειμένου να διερευνηθεί η ύπαρξη contagion μεταξύ του Bitcoin και βασικών κατηγοριών περιουσιακών στοιχείων, όπως οι μετοχές, ο χρυσός, τα ομόλογα και ο δείκτης δολαρίου των ΗΠΑ. Τα αποτελέσματα καταδεικνύουν ότι παρατηρείται σποραδικό και μη συστηματικό contagion, γεγονός που υποδηλώνει ότι το Bitcoin δεν συνιστά πηγή συστημικού κινδύνου. Ωστόσο, η περίοδος της πανδημίας COVID-19 αποτελεί σημείο καμπής στη δομή των συσχετίσεων, καθώς οι δυναμικές συσχετίσεις εμφανίζουν διαφοροποιημένα πρότυπα και εντονότερες διακυμάνσεις, ιδίως όσον αφορά τη σχέση του Bitcoin με την αγορά μετοχών των ΗΠΑ. Το δεύτερο κεφάλαιο επεκτείνει την ανάλυση του πρώτου, προβαίνοντας σε σαφή διάκριση μεταξύ contagion και interdependence. Η ανάλυση πραγματοποιείται στο πεδίο των συχνοτήτων, αξιοποιώντας τη μεθοδολογική προσέγγιση των Bodart και Candelon (2009). Τα αποτελέσματα αναδεικνύουν την ύπαρξη αμφίδρομου contagion αλλά και interdependence μεταξύ του Bitcoin και μεγάλων διεθνών χρηματιστηριακών αγορών, ιδίως των Ηνωμένων Πολιτειών, κυρίως κατά την μεταπανδημική περίοδο. Τα αποτελέσματα αυτά υποδηλώνουν αυξανόμενη ενσωμάτωση στις διεθνείς χρηματοπιστωτικές αγορές του Bitcoin τόσο με ανεπτυγμένες όσο και με αναδυόμενες οικονομίες και αμφισβητούν την υπόθεση του «ασφαλούς καταφυγίου», υποστηρίζοντας ότι συμπεριφέρεται κυρίως ως περιουσιακό στοιχείο κινδύνου. Στο τρίτο κεφάλαιο διερευνώνται οι μακροοικονομικοί προσδιοριστικοί παράγοντες της μεταβλητότητας του Bitcoin μέσω ενός μικτής συχνότητας υποδείγματος GJR-GARCH-MIDAS-X, το οποίο ενσωματώνει 21 μακροοικονομικούς δείκτες. Τα αποτελέσματα δείχνουν ότι δείκτες οικονομικής και χρηματοοικονομικής αβεβαιότητας, η βιομηχανική παραγωγή, οι χρηματοοικονομικές συνθήκες, η ρευστότητα και μεταβλητές που σχετίζονται με τον πληθωρισμό επηρεάζουν σημαντικά τη μακροχρόνια μεταβλητότητα του Bitcoin. Συνολικά, τα ευρήματα της διατριβής υποδεικνύουν ότι το Bitcoin έχει μετεξελιχθεί από ένα σχετικά απομονωμένο ψηφιακό περιουσιακό στοιχείο σε ένα ολοένα και περισσότερο ενταγμένο στο διεθνές χρηματοπιστωτικό σύστημα και εξαρτώμενο από μακροοικονομικές συνθήκες χρηματοοικονομικό μέσο, χωρίς ωστόσο να συνιστά έως σήμερα πηγή συστημικού κινδύνου για το παγκόσμιο χρηματοπιστωτικό σύστημα.
Quang Nghĩa Nguyễn, Tuyen Vu, Minh Thông Phạm, Kien Nguyen · 5 authors
Existing smart contract vulnerability datasets exhibit over 34% train–test overlap due to repeated function-level code, causing models to favor structural memorization over semantic generalization. To mitigate this issue, we construct a benchmark dataset with zero function overlap between the training and test partitions. Furthermore, we introduce GraphFusionDetect (GFD), a novel approach that integrates fine-tuned CodeBERT embeddings with Graph Neural Networks (GNNs) to capture inter-function dependencies. GFD achieves F1-scores of 80% for detecting reentrancy vulnerabilities and 89% for timestamp dependency vulnerabilities, surpassing baseline methods and enabling more robust and generalizable vulnerability detection.
This article discusses the impact of Distributed Ledger Technologies (DLT) and digital assets in financial landscape, emphasizing the evolving role of Certified Management Accountants (CMAs) by 2026. It highlights the transition of blockchain from an experimental tool to a crucial component in finance, improving transparency and automation. The article highlights asset tokenization, illustrated by Pakistan's tokenization of sovereign bonds, and the use of smart contracts in treasury operations, which also pose risks. The emergence of the Pakistan Virtual Asset Regulatory Authority (PVARA) is noted for enhancing regulation and accountability in digital assets. The article also calls for CMAs to adapt traditional accounting principles, develop cybersecurity skills, and strategically manage digital assets, thus redefining Corporate Finance and presenting new challenges and opportunities for accurate financial representation.
Ethereum-Smart Contracts verwalten häufig erhebliche finanzielle Werte. Da sie praktisch unveränderlich sind und häufig böswilligen Akteuren ausgesetzt sind, die durch finanziellen Gewinn motiviert sind, stellt die semantische Korrektheit eine zentrale Sicherheitsanforderung dar. Etablierte Testmethoden reichen oft nicht aus, um die Korrektheit über alle möglichen Ausführungspfade hinweg zu gewährleisten. Daher stellt die formale Verifikation ein wesentliches Mittel dar, um solche Sicherheitsgarantien zu stärken. Diese Arbeit untersucht die auf symbolischer Ausführung basierende Verifikation von Ethereum-Smart-Contracts unter Verwendung des KEVM-Frameworks sowie zweier darauf aufbauender Werkzeuge auf höherer Abstraktionsebene: ACT und Kontrol. Diese Arbeit behandelt Fragestellungen hinsichtlich der Ausdrucksstärke und Konstruktion von Beweisen sowie der Nutzbarkeit und Interpretierbarkeit sowohl von Beweisdefinitionen als auch von generierten Beweisartefakten. Es wird untersucht, ob und welche praktischen Herausforderungen bei der Verwendung von KEVM und zugehörigen Werkzeugen auftreten, einschließlich der Syntax, der verfügbaren Debugging-Werkzeuge sowie der Analyse von Beweisen und Gegenbeweisen. Anschließend erfolgt eine Evaluierung, wie semantische Eigenschaften über alle Werkzeuge hinweg spezifiziert werden können und wie präzise diese spezifiziert werden, wobei insbesondere die Zielkonflikte zwischen unterschiedlichen Abstraktionsebenen hervorgehoben werden. Darüber hinaus verifizieren wir semantische Eigenschaften von ERC20-Token-Smart-Contracts mit besonderem Fokus darauf, ob bestimmte Einträge in der Common Vulnerabilities and Exposures (CVE)-Datenbank tatsächlich korrekt sind oder mithilfe von KEVM widerlegt werden können. Zu diesem Zweck analysieren wir die gemeldete Schwachstelle, formulieren ein formales Argument gegen die behauptete Verletzung und konstruieren darauf aufbauend einen Beweis unter Verwendung von Kontrol. Dabei zeigen wir, wie semantische Eigenschaften innerhalb des Frameworks formuliert und verifiziert werden können. Abschließend untersuchen wir die Community-Aktivität rund um KEVM und dessen Ökosystem. Dazu werden GitHub-Repository-Metriken sowie Kommunikationsdaten aus Discord ausgewertet, um Entwicklungsaktivität, Dynamiken der Beitragenden sowie Muster im Nutzer-Support zu analysieren. Diese kombinierte Perspektive aus technischer und empirischer Sicht liefert eine ganzheitliche Betrachtung von KEVM sowohl als formales Verifikationsframework als auch als Entwickler-Ökosystem.
The rapid advancement of blockchain network protocols has positioned decentralized finance (DeFi) as a key distributed application ecosystem in modern digital infrastructure. These distributed network systems are reshaping traditional financial paradigms by leveraging peer-to-peer protocols for accessible, transparent, and efficient services. However, the underlying network infrastructure faces significant security challenges, particularly concerning transaction manipulation within the framework of Maximal Extractable Value (MEV). MEV has emerged as a critical network security vulnerability due to its exploitation of transaction-ordering mechanisms in blockchain consensus protocols. Despite extensive research on MEV, critical gaps remain in understanding and securing distributed ledger networks against these vulnerabilities across various blockchain platforms. In this paper, we present a comprehensive survey of MEV within DeFi ecosystems through a multi-faceted approach. We provide a detailed taxonomy of MEV attack strategies targeting network protocol vulnerabilities. Furthermore, we offer a categorization of security countermeasures spanning consensus protocols, base-layer network design, and application-level defenses. Additionally, we present an empirical analysis of MEV dynamics across different blockchain networks, quantifying their impact on network performance, security, and fairness. This study contributes to enhancing the security of distributed network applications and advancing more robust and equitable network protocols for decentralized systems.
Muhammad Usman Malik, Muhammad Junaid, Muhammad Aqeel, Muhammad Wasim · 5 authors
This study investigates how blockchain technology can fundamentally transform public value accounting by addressing persistent challenges of transparency, accountability, and stakeholder engagement inherent in traditional systems. Conventional public accounting frameworks are frequently characterized by structural inefficiencies, deliberate opacity, and perverse incentive structures that collectively undermine public trust and hinder effective governance. The research seeks to establish whether blockchain's distinctive technological attributes can provide viable solutions to these systemic problems while creating new paradigms for public value measurement and distribution. The research employs a rigorous mixed-methods methodology that combines computational modeling of blockchain architectures with empirical stakeholder analysis. Quantitative methods include simulation of tokenized governance models and network analysis of transaction transparency in test environments. Qualitative components incorporate in-depth interviews with public sector stakeholders, focus group discussions with citizens, and case study analysis of early blockchain implementations in municipal accounting. The study specifically evaluates three key blockchain features - immutable distributed ledgers, self-executing smart contracts, and programmable tokenization - as foundational elements for next-generation accountability frameworks. The comprehensive analysis yields several significant findings. First, blockchain implementation demonstrates measurable improvements in financial transparency, reducing audit times by an average of 40% in pilot programs. Second, smart contract automation eliminates discretionary interpretation of public spending rules, decreasing compliance violations by 62%. Third, tokenized participation mechanisms correlate with a 35% increase in stakeholder engagement metrics. Most importantly, the research identifies specific design principles for blockchain systems that successfully mitigate value distortion in public accounting while creating alignment between institutional actions and community expectations. This research makes multiple novel contributions to both academic literature and practical governance reform. The study develops the first comprehensive framework for applying blockchain's decentralized architecture to public value accounting, complete with empirically validated design specifications. It introduces the innovative concept of "dynamic tokenization" for real-time value tracking in public goods provision. The work also bridges important theoretical gaps between distributed ledger technologies and public administration theory, offering concrete pathways for implementing more equitable, transparent, and participatory governance models. These findings have significant implications for governments seeking technological solutions to persistent accountability challenges in an increasingly digital public sphere.
This research examines the relationship between attention to ChatGPT and global cryptocurrency ownership using data from 14 countries. Google search volumes for ChatGPT proxy for country-level attention to artificial intelligence (AI). The results show that countries with higher levels of AI-related attention have lower rates of cryptocurrency ownership. This pattern is consistent with the view that investor attention relates to risk preferences and trading behaviour. Overall, the findings offer a potential link between AI attention and asset allocation choices.