Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideals of decentralization and censorship resistance are undermined in practice, motivating ongoing efforts to reestablish these properties. Existing proposals for fairness mechanisms depend on the assumption that a sufficient fraction of block proposers adhere to Ethereum's protocols as intended. We refer to such proposers as altruistic, as this behavior may come at the cost of reduced revenue. Prior analyses indicate that a consistent share of 91 percent of proposers delegate block construction to centralized services, effectively signing externally constructed blocks blindly, and are thus not considered altruistic. To assess whether the remaining 9 percent of proposers genuinely exhibit altruistic behavior, we conducted an empirical analysis and found that an additional 6.1 percent also interact with such external services. Further, we found that less than 1.4 percent of proposers consistently acted in accordance with Ethereum's decentralization and censorship resistance objectives. These findings suggest that relying solely on the mere presence of altruistic proposers is insufficient to ensure that proposed fairness mechanisms reestablish Ethereum's ideals, highlighting the need for additional incentive- or penalty-based mechanisms.
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik · 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideal of censorship resistance, together with related fairness properties, is undermined in practice, motivating fairness mechanisms that aim to restore these properties. Several of these mechanisms hand control over block contents to a committee of proposers under a 1-of-n honest assumption: at least one committee member complies with the mechanism even when deviating would increase personal revenue. We refer to such proposers as altruistic. Yet prior work shows that roughly 91 percent of blocks are constructed by centralized block-building services that demonstrably take user-adverse actions for financial gain; the responsible proposers sign these blocks blindly, without any means of intervention. A common reading of this figure is that 9 percent of proposers forgo these gains and act altruistically. Our empirical analysis of the full year 2025 shows that this share is far smaller: at most 1.55 percent of proposers can plausibly be regarded as altruistic, whereas the remaining 98.45 percent of proposers exhibit observable non-altruistic behavior. We interpret 1.55 percent as an upper bound on the prevalence of altruistic proposers. These results imply that committee-based fairness mechanisms that rely on altruistic members would require substantially larger committees than currently proposed. This raises concerns about their practical viability and motivates mechanisms in which fair behavior is the rational choice.
Justin Wang, Andreas Bigger, Xiaohai Xu, Jiahao Lin · 8 authors
Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in ways that improve security and in ways that might increase risk. We introduce EVMbench, an evaluation that measures the ability of agents to detect, patch, and exploit smart contract vulnerabilities. EVMbench draws on 117 curated vulnerabilities from 40 repositories and, in the most realistic setting, uses programmatic grading based on tests and blockchain state under a local Ethereum execution environment. We evaluate a range of frontier agents and find that they are capable of discovering and exploiting vulnerabilities end-to-end against live blockchain instances. We release code, tasks, and tooling to support continued measurement of these capabilities and future work on security.
The article provides a comprehensive study of the systemic transformation of corporate governance in the context of global digitalization, characterized by the transition from hierarchical models to decentralized structures. It is substantiated that blockchain technology emerges as a new institutional foundation, where traditional bureaucratic verification mechanisms are replaced by algorithms based on cryptographic protocols. A particular emphasis is placed on the distinctions between public (permissionless) and private (permissioned) blockchain networks regarding the immutability of records. The study examines the concept of decentralized governance and the functional specifics of Decentralized Autonomous Organizations (DAOs), where operational logic and management regulations are implemented directly into the software code of smart contracts. This minimizes the influence of traditional administrative management and mitigates "single point of failure" risks. The theoretical framework of the work builds upon classical theories, such as Oliver Williamson’s "Transaction Cost Theory," Michael Jensen and William Meckling’s "Principal-Agent Theory," and the scholarly works of Harold Demsetz. Blockchain is analyzed as a tool that renders market exchange more economically viable than hierarchy. The author proposes an original interpretation of a multi-tier blockchain model for enterprise management, encompassing the infrastructure, network, consensus, data, and application layers. The essence of consensus algorithms (PoW, PoS, DPoS) is disclosed through the prism of management. Special attention is devoted to international experience in legal regulation and the processes of implementing these standards within the legislative framework of Ukraine. The economic effect and practical aspects of the study are analyzed through successful case studies of global corporations (IBM, Amazon, Oracle, Walmart, Nestlé) and Ukrainian business initiatives (TASCOMBANK, SETAM, Agroxy, Softengi). These cases demonstrate a significant reduction in verification costs, lower operating expenses, and increased transparency in supply chains. The transition to an innovative "Management-as-a-Service" paradigm is justified, where blockchain serves not merely as software but as a new firm architecture. Conclusions are drawn regarding a shift in the management ontology – moving from "governance by humans" to algorithmic "governance by code," which ensures data immutability, cyber resilience, and the possibility of real-time preventive risk monitoring. References: 1. Kuzmina, T. O., Berezovskyi, Yu., Kalinskyi, Ye., Arliukova, Yu., & Trofymchuk, A. (2024). 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This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.
Aleksandar Šević, Željko Šević, Athanasios Fassas, Panayiotis Tzeremes
There is a strong impetus to make cryptocurrencies more environmentally friendly, and in our study it is has been analyzed whether commodity price shocks have varying impacts on clean and dirty cryptocurrency interconnectedness before, during and after the COVID-19 pandemic. Using the decomposed and partial connectedness measure we evaluate the connectedness of oil price shocks, demand, supply and risk, as well as five clean and five dirty cryptocurrencies from October 2017 until April 2024. The spikes in demand and disruptions in oil supply lead to price increases. Oil shocks have the largest impact on sampled crypto products during the COVID-19 period, as opposed to pre- and post-pandemic years, and they demonstrate a stronger influence on selected cryptocurrencies than internal crypto-to-crypto dynamics. During the crisis, the difference between clean and dirty cryptocurrencies becomes less relevant when compared to no-crisis periods. We also find that clean cryptocurrencies are net recipients of shocks, while dirty counterparts, dominated by Bitcoin and Ethereum, are net transmitters, especially during the recovery phase. Our findings are relevant for supporting the transition to clean cryptocurrencies and contribute to a better understanding of dynamic interconnectedness. • Examines the decomposed and partial connectedness • Uses time-varying parameter vector autoregression (TVP-VAR) models • Highlights the heterogeneity in cryptos’ responses to oil price fluctuations • Total Connectedness Index peaks during the COVID-19 pandemic • The distinctions between clean and dirty cryptocurrencies reemerged post-COVID
Abstract The peer-reviewed journal article imposes structural constraints on the dissemination, validation, and reuse of research outputs. Intermediate results, negative findings, methodological refinements, and replication attempts are systematically underrepresented in published literature, limiting visibility into ongoing research activity for both scientists and mission-driven funders. Here we present Carrierwave, an open infrastructure for continuous, granular scientific communication built on structured research objects (ROs), cryptographic provenance, blockchain-based attribution, and programmable incentive mechanisms. Each RO represents an atomic unit of scientific output -- a single experimental result, negative finding, dataset, protocol, or replication -- that is hashed for content integrity, stored in a persistent database, and optionally minted as an ERC-721 non-fungible token on the Ethereum blockchain. The system includes an on-chain bounty pool enabling funders to directly incentivize specific research activities, and an automated analysis layer that synthesizes disclosed ROs into continuously updated research landscape maps. We describe the system architecture, report on its implementation and deployment on Ethereum mainnet, and present a quantitative analysis of disease-specific publication frequency demonstrating the information latency problem that Carrierwave addresses. The distribution of publication frequency across disease areas is highly skewed, with the majority of conditions represented by fewer than four publications per year in high-impact biology journals. For diseases in the long tail, the interval between successive publications may span months or years. Publication frequency correlates poorly with disease burden, instead reflecting historical research community size and advocacy momentum. By reducing the unit of communication to the individual research object and eliminating editorial gatekeeping as a prerequisite for disclosure, Carrierwave increases the effective sampling rate of scientific activity in precisely the domains where publication-based visibility is most sparse. The system is live at https://carrierwave.org .
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Yue Li, Lei Wang, Kaixuan Wang, Zhiqiang Yang · 7 authors
The rapid proliferation of autonomous AI agents is driving a shift toward agentic commerce, where agents are expected to autonomously invoke and pay for services. While blockchain-based payments offer a programmable foundation for such interactions, the recently proposed x402 standard fails to enforce end-to-end atomicity across service execution, payment, and result delivery. In this paper, we present A402, a trust-minimized payment architecture that securely binds cryptocurrency payments to service execution. A402 introduces Atomic Service Channels (ASCs), a new channel protocol that integrates service execution into payment channels, enabling real-time, high-frequency micropayments for agentic commerce. Within each ASC, A402 employs an atomic exchange protocol based on TEE-assisted adaptor signatures, ensuring that payments are finalized if and only if the requested service is correctly executed and the corresponding result is delivered. To further ensure privacy, A402 incorporates a TEE-based Liquidity Vault that privately manages the lifecycle of ASCs and aggregates their settlements into a single on-chain transaction, revealing only aggregated balances. We implement A402 and evaluate it against x402 with integrations on both Bitcoin and Ethereum. Our results show that A402 delivers orders-of-magnitude performance and on-chain cost improvements over x402 while providing trust-minimized security guarantees.
In a metaverse ecosystem composed of various sub-metaverses, each offering unique functionalities and use cases, secure cross-domain communication becomes an essential requirement. Traditional authenticated key establishment (AKE) methods typically rely on centralized servers for identity verification, thus introducing single points of failure and significant latency. While some blockchain-based approaches mitigate these issues, they remain vulnerable to malicious key uploads. This paper proposes a blockchain-assisted identity (ID)-based hierarchical key management system and illustrates a cross-sub-metaverse AKE protocol with provable security to solve single points of failure and the risk of malicious key uploads. The hierarchical structure is designed to manage and categorize users’ identities. Moreover, smart contracts are used to pre-verify uploaded user identities and public keys on the blockchain, eliminating the need to fully trust identity issuers and preventing erroneous submissions. We implemented a prototype of our proposed blockchain-assisted cross-domain key management scheme, achieving an average execution time of approximately 0.1 seconds per user operation. We also deployed our contract on the Ethereum test network, incurring 1,802k gas for registration and 1,625k gas for key additions/updates. Furthermore, we formally prove the protocol’s security under the extended Canetti-Krawczyk (eCK) model, highlighting its suitability for next-generation metaverse ecosystems.
Abstract Traceability is an essential practice to ensure transparency, authenticity, and regulatory compliance in modern agricultural supply chains, especially high-value agricultural products. Regarded as the king of fruits in Southeast Asia for its unique taste, texture, and aroma, durian dominates the market of exported fruit commodities. However, recurring issues such as fraudulent GAP numbers, mislabelled origins, premature harvesting, and product tampering undermine consumer trust and export credibility. To address these challenges, this study presents an integrated traceability architecture combining RFID, a MySQL database, an automated Node.js backend, and Ethereum-compatible smart contracts. The developed system enables automated ingestion of physical RFID data, secure on-chain recording via immutable ledger functions, and optional generation of ERC-721 NFTs as digital certificates. Empirical validation includes RFID read-rate testing, blockchain performance measurement, and gas usage analysis. Carton-level tagging, wherein a single RFID tag is attached to a carton rather than each individual fruit, significantly reduces per-durian blockchain cost. The results demonstrate that the proposed architecture is technically robust, flexible, economically scalable, and suitable for SME use in high-value or ultra-premium fresh-produce chains.
• A novel Framework for Secure and Efficient Healthcare Data Management • A Blockchain-Based Identity Management and Access Control • Data Integrity Verification with Merkle Trees • Scalable and Compliant Data Storage • Secure Data Sharing via Proxy Re-Encryption The healthcare sector increasingly relies on digital infrastructures to manage large volumes of sensitive medical data. Ensuring integrity, controlled access, interoperability, and auditability remains a fundamental challenge. We propose BlockHealth, a hybrid blockchain-based framework that integrates smart contracts, distributed databases, and proxy re-encryption to support secure and verifiable healthcare data management. The system leverages Ethereum and NFT-based identities for access control, Merkle-tree commitment for tamper-evident integrity verification, and a distributed Cassandra storage layer for scalable and regulation-compliant off-chain data management. Proxy re-encryption enables secure delegation of access without exposing private keys, while a coordinating API service ensures interoperability with existing hospital infrastructures. Our evaluation demonstrates the feasibility and efficiency of core operations — including hashing, on-chain commits, and re-encryption — indicating that the proposed framework can provide a practical balance among verifiability, performance, and deployability in realistic healthcare environments.
D. Hema Lakshmi, B. Prem Sai Siddhik, B. Akhil Kumar, Ch. Siva Venkata Sai Tharun · 5 authors
Resumes are a key part of traditional hiring, but when human reviewers may not accurately identify the true skills of candidates. Sometimes, when checks are done, fraudulent credentials may pass undetected due to limitations in manual verification. A new method is presented here that uses smart algorithms in a distributed ledger system. By connecting machine learning with secure data records, trust in verifying applicants grows a lot. The proposed system improves efficiency by reducing reliance on traditional keyword-based filtering. The software uses natural language tools to look at the applicant's information, extracts relevant skills and generates a performance score for each candidate. Cryptographic hashes of credentials are stored on a distributed ledger, ensuring that validation cannot be altered or hacked. An online model was created using ReactJS, Flask, MongoDB, and connections to the Ethereum Blockchain. The results show that the method automatically sorts job applicants, quickly checks their documents, and consistently finds qualified people in different fields. Combining smart algorithms with decentralised records increases trust, cuts down on manual tasks, and brings more clarity to the hiring process.
Fatemeh Erfan, Mohammad Yahyatabar, Martine Bellaïche, Talal Halabi
• Created a refined, expanded, and precisely labeled dataset with explanations, risk assessments, and fixes for each vulnerability • Fine-tuned an open-source LLM: LLaMA-3.1-8B using parameter-efficient techniques (LoRA) for smart contract vulnerability detection • Fine-tuned GPT-4o-mini on the same corpus for comparative analysis • Developed a real-time Visual Studio Code (VSCode) plugin integrating GPT-4o-mini for smart contract auditing • Released the datasets, tool, and fine-tuned model to advance research in smart contract security Since the advent of Ethereum, ensuring the security of smart contracts has become imperative. Integer overflow and underflow, reentrancy, and timestamp dependency remain the three most prevalent vulnerabilities in deployed contracts. Existing static-analysis tools often yield insufficient accuracy, and datasets derived from them inherit the same shortcomings. Moreover, the smart contract ecosystem lacks a dependable, real-time auditing aid for developers and a fine-tuned model trained on a truly comprehensive corpus. In this paper, we present three main contributions. (1) Dataset curation: the state-of-the-art vulnerability datasets are aggregated and harmonized, producing a clean, fully labeled dataset that integrates detailed explanations, potential security risks, vulnerable line ranges, code snippets, and corresponding fixes. The dataset is publicly available via our GitHub repository. (2) Model fine-tuning: the LLaMA-3.1-8B model as well as GPT-4o-mini are fine-tuned on this corpus and evaluated with both standard classification metrics and text-quality measures. The fine-tuned LLaMA-3.1 model achieves a precision of 93.55%, an average semantic similarity of 77.48%, and a code similarity of 87.25%. (3) IDE integration: We implement a real-time Visual Studio Code extension, backed by the GPT-4o API, that highlights, explains, and automatically patches vulnerabilities as the developer writes. Together, these contributions deliver a rigorously validated model and a practical developer toolchain that markedly advance the state of smart contract security research and practice.
The growth of digital music streaming platforms has changed the way music is distributed and accessed across the world. These platforms make music easily available to listeners the royalty distribution process still faces several challenges. Limited transparency, delayed payments, and the involvement of multiple intermediaries often reduce efficiency and affect the earnings received by artists. This work shows a blockchain-based framework for music royalty distribution. The given system combines Ethereum smart contracts, Non-Fungible Tokens (NFTs), and the Inter Planetary File System (IPFS) to support secure ownership management and automated royalty payments. Smart contracts execute royalty transactions based on predefined conditions, while NFTs represent ownership of digital music files. IPFS is used for decentralized storage to maintain secure and tamper resistant media files. By reducing dependency on central authorities, the framework makes royalty transactions easier to track and supports fair revenue distribution for artists. The obtained results show fast royalty processing and improved revenue sharing compared with old royalty management systems [18].
Smart contracts (SCs) implemented on blockchain represent a breakthrough in decentralized applications, enabling a range of functions such as managing supply chains and handling elections. As the adoption of SCs increases, the need to detect flaws and vulnerabilities in their execution grows. To address this challenge, we present Branch Reinforcement Learning Fuzzer (BRLF), a deep reinforcement learning-based solution for the detection of vulnerabilities in SCs. The novelty of our method is threefold: first, our deep model uses text-based embeddings of conditional branches to enhance its adaptability and flexibility. Secondly, we propose a reward function that considers multiple aspects of fuzzing, such as opcode analysis and gas usage. Finally, we incorporate evolutionary algorithms into our approach, which significantly bolsters its ability to produce varied outputs. Extensive evaluation on three datasets of Ethereum-based SCs shows that BRLF outperforms state-of-the-art methods, detecting more vulnerabilities and achieving higher code coverage than existing solutions. Our code and data are available at: https://zenodo.org/records/15022152
This review explores the application of machine learning techniques for fraud detection and prevention in the Ethereum blockchain. As a leading platform for decentralized applications (dApps), Ethereum is vulnerable to fraudulent activities such as scams, hacking attempts, and malicious transactions. This paper provides a comprehensive analysis of machine learning models used to predict, detect, and mitigate fraudulent behavior within the Ethereum ecosystem. By overviewing various machine learning methods, this study identifies the most effective approaches for addressing different types of vulnerabilities while offering a thorough review of existing research, key challenges, and limitations. It also examines the datasets and feature engineering techniques applied in this domain, outlining future directions and potential strategies for improving fraud detection. While machine learning has enhanced Ethereum’s security, challenges such as data availability, adversarial attacks, and model interpretability remain significant concerns. To address these gaps, this study highlights the potential of integrating deep learning architectures, graph representations, and hybrid models that combine supervised and unsupervised learning. Additionally, it explores the use of active learning and genetic programming to further enhance fraud detection capabilities. Furthermore, leveraging AI, particularly through large language models, could improve interpretability at the account, block, or transaction level, offering a clearer, more comprehensive view of fraudulent behavior across the Ethereum network. By tackling these challenges, future advancements in machine learning could further strengthen the resilience, security, and trustworthiness of Ethereum’s infrastructure.
Blockchain has gained significant attention in recent years, with smart contracts enabling automated and trustless financial interactions such as decentralized exchanges, tokenized assets, and on-chain governance. Because these programs often control assets of substantial value, a large body of research has focused on detecting security vulnerabilities in smart contracts. However, beyond security, understanding the actual behavior of a contract remains challenging, particularly when source code is unavailable. This work addresses the identification of semantic properties, defined as behavior-level characteristics that describe a contract's purpose based on its state changes and interactions. Detecting such properties can support applications such as regulatory analysis of relevant contracts and the simplification of contract logic by filtering semantically uninformative boilerplate code. The work focuses on static analysis approaches utilizing Datalog.The methodology first includes a systematic literature review to identify existing approaches for the static analysis of Ethereum bytecode using Datalog, as well as related work on semantic properties. The review indicates that Gigahorse is currently the most prominent tool in this category; consequently, it was selected as the basis for the following analysis.Based on this foundation, several function-level properties are defined, including authenticated functions as well as different types of setter and getter functions. In addition, a contract-level property representing a simple token contract is defined. Detection mechanisms for these properties are implemented in Datalog and subsequently evaluated. The results indicate that Gigahorse is generally well-suited for detecting such semantic properties, although practical limitations were encountered, particularly in the form of limited or missing documentation.
Blockchain provides a decentralized and tamper-resistant ledger for securely recording transactions across a network of untrusted nodes. While its transparency and integrity are beneficial, the substantial storage requirements for maintaining a complete transaction history present significant challenges. For example, Ethereum nodes require around 23TB of storage, with an annual growth rate of 4TB. Prior studies have employed various strategies to mitigate the storage challenges. Notably, COLE significantly reduces storage size and improves throughput by adopting a column-based design that incorporates a learned index, effectively eliminating data duplication in the storage layer. However, this approach has limitations in supporting chain reorganization during blockchain forks and state pruning to minimize storage overhead. In this paper, we propose COLE$^+$, an enhanced storage solution designed to address these limitations. COLE$^+$ incorporates a novel rewind-supported in-memory tree structure for handling chain reorganization, leveraging content-defined chunking (CDC) to maintain a consistent hash digest for each block. For on-disk storage, a new two-level Merkle Hash Tree (MHT) structure, called prunable version tree, is developed to facilitate efficient state pruning. Both theoretical and empirical analyses show the effectiveness of COLE$^+$ and its potential for practical application in real-world blockchain systems.
Abstract The rapid expansion of cryptocurrency markets has significantly transformed global financial systems through the adoption of decentralized, blockchain-based transaction mechanisms. Digital assets such as Bitcoin and Ethereum operate on distributed ledger technology, which enhances transparency, immutability, and peer-to-peer verification without reliance on traditional financial intermediaries. Despite these technological advancements, the cryptocurrency ecosystem faces escalating cybersecurity risks that threaten the integrity of financial data and reporting systems. Cryptocurrency exchanges, digital wallets, custodial services, and decentralized finance (DeFi) platforms are increasingly targeted by cybercriminals through hacking, phishing schemes, ransomware attacks, private key theft, and smart contract vulnerabilities. These cybersecurity incidents have profound implications for financial record integrity, including unauthorized transactions, asset misappropriation, valuation distortions, and inaccuracies in financial statements. Unlike conventional banking systems, cryptocurrency transactions are often irreversible, amplifying the financial and accounting consequences of cyber breaches. Furthermore, the pseudonymous nature of blockchain transactions complicates audit verification, regulatory compliance, and internal control processes. As organizations integrate digital assets into their financial reporting frameworks, weaknesses in cybersecurity governance may undermine stakeholder confidence and market stability. This paper critically examines the major cybersecurity threats present in cryptocurrency markets and evaluates their direct and indirect impact on the reliability, accuracy, and auditability of financial records. It also analyzes existing risk mitigation strategies, including multi-factor authentication, cold storage solutions, encryption protocols, smart contract audits, and regulatory oversight mechanisms. The study concludes that while blockchain technology inherently promotes data immutability and transparency, systemic vulnerabilities at exchange, platform, and user levels continue to pose substantial risks. Strengthened cybersecurity governance frameworks, standardized accounting treatments for digital assets, and coordinated global regulatory efforts are essential to ensuring the long-term integrity and sustainability of cryptocurrency-based financial systems.
The global logistics sector is confronted with crucial data reliability challenges wherein traditional centralized systems have a 15-20% manual error rate and are highly susceptible to counterfeiting. In this regard, the current research proposes Sentinel, a decentralized supply chain tracking framework utilizing the Polygon Proof-of-Stake blockchain coupled with smart contracts in Solidity for granting immutability to data governance. It follows a hybrid architecture wherein on-chain cryptographic verification is coupled with MongoDB for high-speed off-chain data retrieval. Extensive performance testing was performed on a simulated supply chain network with 10,000 transaction cycles of creation, transfer, and delivery. It shows that Sentinel has been able to achieve 100% in data integrity, thus rejecting all 500 unauthorized ledger modifications attempted during security stress testing. In terms of efficiency, the proposed framework minimized data retrieval latency to less than 180 ms, which was an improvement of 92% compared to traditional decentralized architectures. Additionally, it minimized the transaction cost to roughly ₹0.45/unit, thus offering a cost reduction of about 99.9% compared to traditional Ethereum Layer-1 implementations.
We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.
Decentralised finance (DeFi) has profoundly reshaped global capital markets, enabling automatic transactions, eliminating the need for intermediaries, and accelerating transaction settlement times. Despite these significant advancements, institutional involvement in DeFi remains very low. The lack of institutional participation can be attributed to the lack of an enforceable compliance mechanism at the protocol level; that is, once a transaction is confirmed as having been completed on the blockchain, it cannot be undone or disputed in any meaningful way. The existing compliance mechanisms are primarily retrospective, meaning that they generate alerts after a transaction has occurred instead of preventing illicit transfers in advance. Regulated financial institutions that transact in cryptocurrency bear the ultimate financial risk and regulatory burden. The UK FCA has made it very clear through CP25/41 that there are now specific regulatory expectations regarding the existence of adequate pre-settlement controls [2]. We introduce AMTTP Version 4.0, which has been designed to have a four-layer architecture explicitly intended to support deterministic compliance enforcement in DeFi institutions. Layer I provides SDKs, REST APIs, and web applications intended for programmatic and human interaction with AMTTP; Layer II provides a compliance orchestration layer that combines (i) machine learning risk scoring (ii) graph analysis (iii) sanctions screening, and (iv) policy adjudication into a single deterministic decision-making matrix; Layer III consists of an offline training pipeline with a Composite Teacher that uses an AutoencoderEnhanced XGBoost (w = 0.4), seven FATF AML Mode Patterns (w = 0.3), and graph structural properties (w = 0.3) in order to produce pseudo-labels (SLPs) for the Student pipeline across 2,640,000 transactions; and finally, Layer IV supports the physical infrastructure for AMTTP deployment, which consists of 18 smart contracts on Ethereum Sepolia, 17 containerised microservices, and a Database Persistence Tier (MongoDB, Redis, Memgraph, IPFS). The Infrastructure Security features multioracle threshold signatures, replay protection & zkNAF a zeroknowledge proof framework that allows for privacy preserving verification of KYC credentials, risk ranges & non-membership from sanctions. In addition, TLS Encryption, Rate Limiting, Cloudflare Tunnel integration & the UI Integrity Service provide an additional layer of protection at the infrastructure level. This paper aims to demonstrate that deterministic compliance can be integrated into decentralised finance at an architectural level. In order to support this assertion, the client SDKs (TypeScript and Python) are released as open source.1