This study introduces the Multilayer Token Network (MLTN), a mathematical framework for analyzing Ethereum token transfers while capturing inter-token transformations crucial to Decentralized Finance (DeFi). Focusing on prominent fund accounts, we propose the PageRank-CheiRank Trade Balance (PCTB), an econometric measure inspired by balance sheet principles to quantify trade behavior over time. Applying MLTN to 2018â2024 transaction data, we reveal Alameda Researchâs evolving trade strategies, fund interdependencies, and token-specific accumulation and distribution patterns, offering new insights into on-chain financial activities.
With the rapid expansion of blockchain applications, smart contracts are becoming increasingly complex, making the automated detection of contract vulnerabilities more critical than ever. Large language models, due to their advanced code comprehensive ability, are considered to have the potential to undertake the task of automated software vulnerability discovery. Although there have been empirical studies on ChatGPT's automated discovery of contract vulnerabilities, the current empirical research has not addressed how well ChatGPT can detect logical vulnerabilities in smart contracts or whether ChatGPT's detection performance for logical vulnerabilities can be improved. To fill this gap, this study collected and organized seven types of logical vulnerability source codes from 6165 real smart contract audit reports and three datasets, such as Web3Bugs, and used this database to validate ChatGPT's detection capability for logical vulnerabilities. To improve ChatGPT's accuracy in detecting logical vulnerabilities, we fine-tuned ChatGPT with a dataset marked with a specific method, achieving an average accuracy rate of 95% for single vulnerability detection per training session. We improved the original marking method to increase further the number of vulnerabilities that a single model can detect. We used a specific completion marking format, ultimately enabling ChatGPT to detect various logical vulnerabilities. In terms of enhancing model scalability, we found a special training set marking method that allows for the addition of detectable vulnerability types through secondary training.
This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness and show their effectiveness.
The increasing demand for scalable blockchain has driven research into parallel execution models for smart contracts. Crystality is a novel smart contract programming language designed for parallel Ethereum Virtual Machines (EVMs), enabling fine-grained concurrency through Programmable Contract Scopes and Asynchronous Functional Relay. This paper presents the first formal structural operational semantics for Crystality, providing a rigorous framework to reason about its execution. We mechanize the syntax and semantics of Crystality in the theorem-proving assistant Coq, enabling formal verification of correctness properties. As a case study, we verify a simplified token transfer function, demonstrating the applicability of our semantics in ensuring smart contract correctness. Our work lays the foundation for formally verified parallel smart contracts, contributing to the security and scalability of blockchain systems.
The aim of this study was to investigate the factors affecting the occurrence of walnut canker disease and the effect of low-temperature treatment on its occurrence in Hotan County, and provide a theoretical basis for the prevention and control of the disease. The occurrence of canker disease was investigated in walnut plantations of different varieties, plant and row spacing, and diameter at breast height (DBH) from April to August of 2023 and 2024. In laboratory research, the spread area of disease spots on isolated branches of peach was studied under different temperature treatments and different treatment times. The occurrences of walnut canker for the two consecutive years were basically the same. The number of new lesions showed an upward trend in April and May and reached its highest in May. The number of new lesions tended to stabilize in June and July in Hotan County. The walnut variety with more severe disease was âXinfengâ. The disease was most serious in plots with plant spacing and row spacing of 10 m Ă 7 m. The DBH of the more susceptible walnut trees was >30 cm. Low-temperature treatment and treatment time had a significant impact on the walnut canker lesion expansion area (p
ABSTRACT This study employed both bibliometric analysis and a comprehensive review of the existing literature to examine 3844 publications in cryptocurrency research, which were collected from the Web of Science Core Collection Database. The study has utilized bibliometric methods to analyze the most productive countries and regions, research institutions, and authors in cryptocurrency research. Cluster analysis of coâcitation articles indicates three main themes in cryptocurrency research over the past decade: the efficiency of the cryptocurrency market, innovation, application, and governance of blockchain technology as well as risk management of cryptocurrencies. Keyword coâoccurrence analysis reveals three major future research directions regarding cryptocurrency: (1) using machine learning methods to forecast price returns of cryptocurrencies; (2) how to enhance the security, legitimacy, and environmental sustainability of cryptocurrencies; (3) further exploration of the impact of various unexpected events on the risks of cryptocurrencies under global instability. In the section of literature review, two to three representative papers from the five mostâcited authors in cryptocurrency research are summarized. Additionally, 28 of the most noteworthy papers, selected based on three different criteria, are presented. These papers cover different periods and research topics, and a brief yet comprehensive overview of these 28 influential papers is provided.
Proof-of-Stake (PoS) is a prominent Sybil control mechanism for blockchain-based systems. In "e-PoS: Making PoS Decentralized and Fair," Saad et al. (TPDS'21) introduced a new Proof-of-Stake protocol, e-PoS, to enhance PoS applications' decentralization and fairness. In this comment paper, we address a misunderstanding in the work of Saad et al. The conventional Proof-of-Stake model that causes the fairness problem does not align with the general concept of Proof-of-Stake nor the Proof-of-Stake cryptocurrencies mentioned in their paper.
Purpose This study aims to analyse the academic literature on cryptocurrency regulation using a combined bibliometric and systematic literature review approach, focusing on research trends, influential contributions and thematic clusters from 2018 to 2024. Design/methodology/approach This study reviews 62 journal articles published between 2018 and 2024. A combined bibliometric and systematic literature review approach is used to analyse key articles, journals, authors and countries contributing to the field. Thematic clusters such as crowdfunding, FinTech, blockchain vulnerabilities, Central Bank Digital Currencies (CBDCs) and economic forecasting in developing countries are identified. Findings The analysis reveals emerging trends and significant advancements in cryptocurrency regulation, highlighting key contributors in the field. Thematic clusters show a focus on blockchain vulnerabilities, the rise of CBDCs, and regulatory challenges in developing economies. These themes represent the most pressing areas in cryptocurrency market regulation. Practical implications The findings offer insights for policymakers, researchers and industry practitioners to shape effective regulatory frameworks, addressing critical issues such as blockchain security and central bank digital currencies. Social implications This research contributes to the development of robust regulatory frameworks, promoting market stability and transparency, which will ultimately benefit global financial markets and stakeholders in the cryptocurrency ecosystem. Originality/value To the best of the authorsâ knowledge, this study is the first to integrate bibliometric and systematic literature review methods to examine cryptocurrency regulation, providing a comprehensive overview of the research landscape.
Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detection accuracy. In this paper, we systematically examine these issues by analyzing and contrasting explicit transactional features and implicit graph-based features, both experimentally and analytically. We explore how different feature sets impact the performance of phishing detection models, particularly in the context of Ethereum's transactional network. Additionally, we address key challenges such as class imbalance and dataset composition and their influence on the robustness and precision of detection methods. Our findings demonstrate the advantages and limitations of each feature type, while also providing a clearer understanding of how feature affect model resilience and generalization in adversarial environments.
Blockchain technology anchors decentralized systems by enabling secure, transparent data management across distributed networks, powering a wide range of applications from foundational cryptocurrencies like Bitcoin to the recently emerging tokenization of real-world assets (RWAs), such as property and commodities. However, its scalability and environmental sustainability depend on consensus mechanisms that maintain network integrity without imposing excessive computational or energy burdens. Proof of Work (PoW), a prevalent mechanism seen in Bitcoin, relies on miners performing energy-intensive cryptographic computations to ensure robust security, yet driving significant resource demands. In contrast, Proof of Stake (PoS) selects validators based on the amount of cryptocurrency they stake, as exemplified by Ethereum post-Merge, providing a markedly more energy-efficient option than PoW. While PoW excels in delivering decentralized security through computational effort, it does so at the cost of high energy consumption; PoS, meanwhile, enhances participation accessibility and reduces resource use but introduces potential centralization risks due to wealth concentration among larger stakers. The rapid rise of artificial intelligence (AI) models, with their substantial energy consumption, underscores a growing strain on computational resources. Hence, it inspires us to propose a new consensus mechanism, namely, Proof of Useful Intelligence (PoUI). PoUI is a hybrid consensus mechanism where workers execute AI-based tasks, such as natural language processing or image analysis, to earn coins, which are then staked to secure the network, seamlessly integrating security with real-world utility. This system leverages decentralized functional nodes, i.e., job posters who submit tasks, market coordinators who oversee jobs distribution, workers who perform computations, and validators who ensure accuracy, all orchestrated by smart contracts for task execution and reward allocation. Our energy analysis benchmarks PoW at$\mathbf{3. 5 1 ~ k W h} / \mathbf{m i n e r}$, PoS at$\mathbf{0. 1 ~ k W h} /$validator, and PoUI at$\mathbf{0. 6} \text{kWh} /$worker - yielding a 97 % energy reduction from PoW while adding value. Simulations further demonstrate that PoUI's dynamic reward adjustment regulates worker participation in the job market, which subsequently encourages a sufficient number of validators in the network.
The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potential of AI agents and agentic systems to reshape industries by decentralizing decision-making, redefining organizational structures, and enhancing cross-functional collaboration. Specific applications include healthcare systems capable of creating adaptive treatment plans, supply chain agents that predict and address disruptions in real-time, and business process automation that reallocates tasks from humans to AI, improving efficiency and innovation. However, the integration of these systems raises critical challenges, including issues of attribution and shared accountability in decision-making, compatibility with legacy systems, and addressing biases in AI-driven processes. The paper concludes that while agentic systems hold immense promise, robust governance frameworks, cross-industry collaboration, and interdisciplinary research into ethical design are essential. Future research should explore adaptive workforce reskilling strategies, transparent accountability mechanisms, and energy-efficient deployment models to ensure ethical and scalable implementation.
Online social platforms for digital communication necessitate an in-depth understanding of their evolving dynamics, especially after the renewal requests brought about by new paradigms, such as Web3. The dynamics within online social networks (OSNs) are influenced by numerous factors, encompassing user behavior, content generation, platform features, and technological advancements, with triadic closure standing out as a prominent and influential element. In this study, we focus on the temporal aspects of triadic closure and its role in the evolution of OSNs, especially after the advent of the Web3 paradigm. By analyzing networks with timestamped links from diverse platforms based on different architectures, including communication, Web3-based, and trade networks, we developed a comprehensive analytical pipeline to support the study of triadic closure patterns. This pipeline includes an algorithm for the census of time-ordered triads, a vector-based model for representing growing networks (growth triadic profile), the identification of triadic closure rules (TERs), and the evaluation of the speed of the formation of closed triads. Our findings reveal significant variations in the impact of triadic closure across different OSNs, marked by diverse growth triadic profiles and varying formation speeds of closed triads as well as diversity in the predictability of evolutionary patterns based on triads. This study not only enhances the comprehension of triadic closure in the temporal evolution of OSNs but also provides valuable insights to be taken into account for the design and administration of online social platforms.
The emergence of Decentralized Finance (DeFi) has introduced a paradigm shift in global financial intermediation, challenging traditional banking systems with transparent, algorithm-driven, and blockchain-based financial services. As institutional investors and banks navigate increasingly complex cross-jurisdictional regulatory environments, DeFi presents an opportunity to reimagine asset securitization through decentralized, programmable frameworks. This paper explores the integration of DeFi protocols into institutional asset securitization, focusing on how smart contracts, tokenization, and decentralized liquidity pools can streamline processes, enhance transparency, and reduce reliance on intermediaries across disparate legal and regulatory jurisdictions. At a broader level, the study outlines the limitations of conventional securitizationâsuch as opacity, time lags, and fragmentationâespecially in multinational banking ecosystems. It then narrows in on how DeFi tools like automated market makers (AMMs), decentralized exchanges (DEXs), and overcollateralized lending protocols can be adapted to structure, issue, and trade tokenized asset-backed securities (ABS). Particular attention is paid to the challenges of legal enforceability, compliance, and interoperability between blockchain platforms and regulatory frameworks. Case scenarios and pilot initiatives are analyzed to demonstrate the feasibility of decentralized securitization in cross-border finance, including synthetic credit instruments and on-chain risk analytics. The paper further examines how oracles and compliance layers (e.g., KYC/AML-integrated smart contracts) can reconcile DeFi's permissionless nature with institutional governance standards. The findings support a hybrid finance future, where regulated entities harness DeFi infrastructure for secure, compliant, and efficient asset securitization. Policy recommendations are offered to foster collaboration between regulators, financial institutions, and protocol developers in building trust-minimized, scalable, and cross-jurisdictionally aligned financial ecosystems.
Modern password hashing remains a critical defense against credential cracking, yet the transition from theoretically secure algorithms to robust real-world implementations remains fraught with challenges. This paper presents a dual analysis of Argon2, the Password Hashing Competition winner, combining attack simulations quantifying how parameter configurations impact guessing costs under realistic budgets, with the first large-scale empirical study of Argon2 adoption across public GitHub software repositories. Our economic model, validated against cryptocurrency mining benchmarks, demonstrates that OWASP's recommended 46 MiB configuration reduces compromise rates by 42.5% compared to SHA-256 at \$1/account attack budgets for strong user passwords. However, memory-hardness exhibits diminishing returns as increasing allocations to RFC 9106's 2048 MiB provides just 23.3% (\$1) and 17.7% (\$20) additional protection despite 44.5 times greater memory demands. Crucially, both configurations fail to mitigate risks from weak passwords, with 96.9-99.8% compromise rates for RockYou-like credentials regardless of algorithm choice. Our repository analysis shows accelerating Argon2 adoption, yet weak configuration practices: 46.6% of deployments use weaker-than-OWASP parameters. Surprisingly, sensitive applications (password managers, encryption tools) show no stronger configurations than general software. Our findings highlight that a secure algorithm alone cannot ensure security, effective parameter guidance and developer education remain essential for realizing Argon2's theoretical advantages.
This study proposes a prototype for locating important individuals and financial exchanges in networks of people trafficking that have grown during the conflict between Russia and Ukraine. It focuses on the role of digital platforms, cryptocurrencies, and the dark web in facilitating these operations. The research maps trafficking networks and identifies key players and financial flows by utilizing open-source intelligence (OSINT), social network analysis (SNA), and blockchain analysis. The results show how cryptocurrencies are used for anonymous transactions and imply that upsetting central coordinators may cause wider networks to become unstable. In order to combat human trafficking, the study emphasizes the significance of real-time data sharing between international law enforcement. It also identifies future directions for the development of improved monitoring tools and cooperative platforms.
The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a \textit{Simplified Peer-to-Peer Swarm Learning (P2P-SL) Framework} tailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust model synchronization while maintaining data privacy. Applied to cancer histopathology, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework's efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications.
The increasing interconnectivity of devices on the Internet of Things (IoT) introduces significant security challenges, particularly around authentication and data management. Traditional centralized approaches are not sufficient to address these risks, requiring more robust and decentralized solutions. This paper presents a decentralized authentication protocol leveraging blockchain technology and the IPFS data management framework to provide secure and real-time communication between IoT devices. Using the Ethereum blockchain, smart contracts, elliptic curve cryptography, and ASCON encryption, the proposed protocol ensures the confidentiality, integrity, and availability of sensitive IoT data. The mutual authentication process involves the use of asymmetric key pairs, public key registration on the blockchain, and the DiffieâHellman key exchange algorithm to establish a shared secret that, combined with a unique identifier, enables secure device verification. Additionally, IPFS is used for secure data storage, with the content identifier (CID) encrypted using ASCON and integrated into the blockchain for traceability and authentication. This integrated approach addresses current IoT security challenges and provides a solid foundation for future applications in decentralized IoT environments.
Mayur Patel, Aditya Vishwakarma, Mohammad Kaif, Shahan Ali
Abstract: Online blockchain-based certificate generation and validation represent a crucial advancement in enhancing transparency, security, and efficiency within government operations. This system enables government organizations to securely issue, verify, and manage certificates, ensuring the integrity of essential documents such as birth certificates, educational diplomas, business licenses, and other critical records. The integration of blockchain technology into certificate management systems can significantly streamline government services while safeguarding against fraudulent activities, document tampering, and administrative errors.In recent years, however, blockchain technology has emerged as a promising solution to address these issues, offering a decentralized, tamper-proof system for the generation and validation of certificates. Blockchain, which is essentially a distributed ledger, stores data across a network of nodes, making it virtually immutable and highly resistant to alterations. Each record or transaction on the blockchain is cryptographically secured, ensuring that once a certificate is issued and recorded, it cannot be modified or deleted without detection
ABSTRACT Blockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchainâbased scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the NonâOrthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the stateâofâtheâart offloading schemes.
Ăngela Filipa Oliveira Gonçalves, Shafik Faruc Norali, Clemens Bechter
The paper investigates current and future pricing models in the European healthcare sector. European countries follow a universal healthcare system, whereas the United States rely on a mix of private insurers, government programmes, and private payments. It is becoming obvious that the European âfreeâ healthcare systems are not sustainable in the long run. The authors propose a private Buy-Now-Pay-Later (BNPL) alternative. BNPL is common practice in retailing but highly unusual in healthcare. The authors suggest to enhancing BNPL further by adding AI and blockchain/crypto technology. However, there are three hurdles to overcome, namely, cryptocurrency volatility, regulatory uncertainty, and adoption barriers. Our field research investigated the acceptance barriers especially whether European medical service providers would accept cryptocurrency payments and the BNPL model in general. Our survey is based on 366 European medical service providers, mainly medical doctors. The results show that there is willingness to accept cryptocurrencies. As recommendation we outline how a fully integrated AI-powered BNPL model with cryptocurrency payments and smart contracts including BNPL Tokenisation in a decentralised financial market could work to the benefit of all stakeholders.
Mahmood A. Bazel, Fathey Mohammed, Mazida Ahmad, Abdullah O. Baarimah · 5 authors
Blockchain technology has gained significant attention in several sectors owing to its distributed ledger, decentralized nature, and cryptographic security. Despite its potential to reform the healthcare industry by providing a unified and secure system for health records, blockchain adoption remains limited. This study aimed to identify the factors influencing the intention to adopt blockchain in healthcare by focusing on healthcare providers. A theoretical model is proposed by integrating the Technological-Organizational-Environmental framework, Fit-Viability Model, and institutional theory. A quantitative approach was adopted and data were collected through an online survey of 199 hospitals to evaluate the model. The collected data were analysed using PLS-SEM. The results indicated that technology trust, information transparency, disintermediation, cost-effectiveness, top management support, organizational readiness, partner readiness, technology vendor support, fit, and viability significantly and positively influenced the intention to adopt blockchain-based Health Information Systems in hospitals. Conversely, coercive pressure from the government negatively affects adoption decisions. Moreover, the study found that the hospital ownership type did not moderate the relationship between the identified factors and blockchain adoption. This study provides valuable insights into the various factors that influence blockchain adoption in hospitals. The developed model offers guidelines for hospitals, blockchain providers, governments, and policymakers to devise strategies that promote implementation and encourage widespread adoption of blockchain in healthcare organizations.
The integration of artificial intelligence (AI) into Internet of Things (IoT) systems has outpaced the development of mechanisms to explain and audit automated decisions, creating a transparency gap. This paper addresses the research problem of establishing immutable audit trails for AI-driven IoT decisions to enhance trust, accountability, and regulatory compliance. We propose a blockchain-based framework that logs each AI inference and its provenance data (inputs, model parameters, and outputs) on a tamper-proof distributed ledger, ensuring every decision is traceable and auditable. The technical method- ology centers on a permissioned blockchain ledger deployed alongside IoT infrastructure. IoT devices and edge nodes commit decision records via smart contracts, producing an im- mutable, timestamped log resistant to manipulation. This approach leverages blockchain’s decentralization and cryptographic integrity to guarantee non-repudiation and data integrity. We detail how the system design balances transparency with privacy (e.g. hashing personal data) to remain compliant with data protection norms. The solution aligns closely with emerging regulatory frameworks such as the EU AI Act’s mandate for automated decision logs and traceability, and GDPR’s accountability and transparency requirements (e.g. maintaining audit logs of AI decisions for explainability). We demonstrate the frame- work’s applicability across domains: healthcare IoT, to log diagnostic AI recommendations for accountability; and industrial IoT, to track autonomous control actions - showing that our approach generalizes to diverse high-stakes environments. The paper’s contributions include a novel architecture for AI decision provenance in IoT, a detailed implementation on a blockchain ledger to securely record AI decision-making processes, and an evaluation of its performance and compliance benefits. By providing a reliable, immutable audit trail for AI in IoT, this work enhances transparency and trust in autonomous systems and offers a timely solution for auditable AI in an era of increasing regulatory scrutiny.
Abstract-Non-Fungible Tokens (NFTs) have emerged as a transformative force in the digital economy, offering creators a novel and decentralized way to monetize their work. Powered by blockchain technology, NFTs ensure transparency, traceability, and ownership of digital assetsâranging from art and music to virtual real estate. By eliminating the dependency on traditional intermediaries such as galleries and auction houses, NFTs empower artists to connect directly with global audiences through dedicated marketplaces. This project explores the core concepts of NFTs, their evolution, and their underlying architecture, including blockchain, smart contracts, token standards, and NFT marketplaces. The work process involves the detailed study of how NFTs are minted by uploading digital assets onto a blockchain-supported marketplace, registered through smart contracts, and then traded securely between users. The project also outlines key components such as tokenization, metadata storage, and transaction validation through cryptographic proofs. Through a comprehensive timeline, technical breakdown, and real-world use cases, the paper emphasizes the growing significance of NFTs in redefining digital ownership, while also evaluating their future impact on the Indian market and beyond. Keywords â NFT, Token, Blockchain, Market, Asset, Ethereum, Fungible.