Mohammad Y. Allaho, Mehmet H. Karaata, Israa A. Elgemiei
The distributed ledger systems rely heavily on miners, who are a vital component of the cryptocurrency ecosystem. Most cryptocurrencies cease to exist within five years of operation [1] due to churning. Most current cryptocurrency analyses in the literature focus on mining pools and ignore the individual miners’ perspective and in-depth analysis of the churning phenomenon and its possible reasons. In this study, we conducted a longitudinal and overall study on two of the most growing cryptocurrency networks, namely Bitcoin and Ethereum. The Bitcoin dataset used spans over 12 years (2009-2021). Whereas the Ethereum dataset spans over 8 years (2015-2023), including the two versions of Ethereum (before and after the merge). Our goal is to uncover the factors that drive miners’ churning and reveal essential characteristics of cryptocurrency mining, such as network fairness and centrality. Generally, both networks experience a decline in active miners over time. Our results confirm the centrality of the Bitcoin and Ethereum networks, whereas Bitcoin is found to be more distributed and fairer than Ethereum in both versions. Also, in Bitcoin, solo miners are less centralized and experience a fairer distribution of blocks formation than pool miners, however, pool miners have more mining rewards on average. Also, pools are found to decrease churning for pool miners compared to solo miners. Moreover, it is found that miners’ waiting time is a significant factor in miners’ churning. The existing protocols used require improvements to increase network decentralization and fairness, as well as reduce miners’ churn.
Shereen Ismail, Bashar Othman, Hassan Reza, Eden Teshome Hunde
Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry.
Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.
To address the challenges of topological obscurity and extreme label sparsity in large-scale Ethereum transaction networks, a novel self-supervised phishing detection framework named Eth-GBAV is proposed, integrating graph attention, broad learning, and adversarial variational inference. The framework initiates with a biased random walk strategy guided by transaction intensity and temporal dynamics to capture the initial behavioral semantics of nodes. To distill discriminative features from noisy backgrounds, a “Generative-Attention” encoding architecture is constructed, where a graph attention network aggregates weighted structural neighborhoods and a Variational Autoencoder (VAE) characterizes the underlying probability distribution of legitimate transaction patterns. By maximizing the evidence lower bound, anomalous accounts are effectively isolated through reconstruction residuals. Furthermore, the broad learning system is introduced as an efficient analytical decision layer. By mapping VAE-derived latent embeddings and reconstruction errors into an expanded high-dimensional feature space, the framework captures intricate behavioral correlations via mapping and enhancement neurons. Extensive experimental verification on two large-scale datasets demonstrates the superior performance of Eth-GBAV. On the XBlock dataset, it achieves a leading F1-score of 0.9847 and a recall of 0.9839, outperforming the most competitive state-of-the-art model by significant margins. On the Kaggle dataset, the framework maintains high robustness with an accuracy of 0.9592 and an F1-score of 0.9069.
Smart contracts are a core component of blockchain-based systems, enabling decentralized applications to autonomously manage assets and enforce program logic. However, vulnerabilities in smart contracts can cause severe financial losses because of their immutability and public accessibility. As a result, analyzing common attack vectors and evaluating static detection techniques before deployment remain critical challenges in blockchain security. This thesis presents an analytical study of high-impact smart contract attack classes and evaluates the effectiveness of AST-based static detection approaches for Ethereum smart contracts. Building on the Aderyn static analysis framework, custom detectors are implemented to analyze structural code patterns that enable reentrancy attacks, authorization bypass vulnerabilities, and unsafe proxy delegation and storage collision risks. Rather than proposing new defensive mechanisms, this work systematically analyzes how known attacks arise from insecure smart contract programming practices and examines how defensive coding patterns can be identified at the source-code level through static analysis. The effectiveness of the analysis is evaluated using vulnerable smart contract implementations and corresponding exploit scenarios developed with the Foundry testing framework. Experimental results show that the implemented detectors successfully identify exploitable vulnerabilities and demonstrate a strong correspondence between attack-enabling code structures and statically detectable patterns, confirming alignment between static analysis findings and real-world attack behavior. This work demonstrates that extensible AST-based static analysis provides a practical foundation for analyzing both smart contract attacks and the defensive patterns intended to mitigate them before deployment.
For communication scenarios demanding extremely high information security and facing significant risks of data leakage, a covert communication scheme based on Ethereum virtual machine bytecode was proposed. By strategically allocating the storage space of smart contract variables, the scheme embedded covert data into contract bytecode and utilized the inherent characteristics of bytecode to set positioning markers, enabling efficient extraction by the receiver. Additionally, three ciphertext parsing modes were designed to accommodate transmissions of different data scales, further enhancing the security of encoded data. Theoretical analysis and extensive experimental results demonstrate that the scheme can effectively hide up to 170 bit of information per transaction. The structural similarity of opcode frequency distributions between the embedded contract and the original contract reaches up to 99.78%. The Pearson correlation coefficient of the high-frequency 3-gram opcode patterns between the normal and embedded contracts is 0.999 7 (<italic>p </italic>= 6.42×10⁻¹⁴), indicating that the embedding process does not introduce statistically significant differences in the local instruction sequence distribution. These results fully validate the strong concealment capability, transmission efficiency, and security of the proposed scheme.
Open access
Internet Traffic Analysis and Secure E-voting
Physical Unclonable Functions (PUFs) and Hardware Security
Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.
Blockchain technologies are making it possible to develop crypto-currencies and programmable smart contracts that can work in worldwide trustless and decentralized environments. Decentralized autonomous organizations (DAOs) that can coordinate the works of crowds of users, developers, and researchers can be built using smart contracts on blockchains. We contribute a decentralized autonomous software organization model and an Ethereum blockchain-based smart contract named AutonomousSoftwareOrg that provides a continuously operating virtual organization for open-source software development communities and users. AutonomousSoftwareOrg provides a project funding mechanism based on crypto-currencies, a decision-making mechanism based on voting, and recordkeeping for software usage citations and executions. Furthermore, software executions, along with their input and output data files, can also be transactionally recorded in AutonomousSoftwareOrg. This enables software execution graphs to be constructed for analysis. An AND/OR graph model of input/output data and software executions is presented, along with analysis algorithms for execution traceability and reproducibility assessment. AutonomousSoftwareOrg is deployed and tested on the Ethereum-based Bloxberg blockchain network which is operated by academic and research institutions, demonstrating its practical viability for sustainable open-source software development.
Mohd Saleem, Sohrab, Matloob Ullah Khan, Faizan Khan Sherwani
Key components of blockchain technology, DeFi represent a revolutionary advance in digital contracts and automated trades, and they are integrated into decentralized networks such as Ethereum. These self-executing contracts eliminate the need for middlemen by autonomously enforcing specified terms. This paper offers a thorough analysis of Decentralized Finance (DeFi), smart contracts, covering their underlying theories, technological foundations, wide range of applications, and ramifications in context of financial inclusion and investment. In order to clarify the workings and practical applications of such innovations, the research technique comprises a methodical evaluation of the literature, an examination of case studies, and an amalgamation of empirical data. This study evaluates their effects on efficiency, transparency, and trust in international transactions by looking at how they are revolutionizing industries like finance, and decentralized governance. It also thoroughly examines security considerations, including best practices and vulnerabilities, as well as regulatory issues and new developments.
The Landauer principle motivates the definition of economic temperature as the monetary price of processing a bit irreversibly. No empirical test of this definition exists in transparent fee markets. This paper fills that gap using daily Bitcoin and Ethereum data, constructing canonical thermodynamic state variables and evaluating five diagnostic layers: state variable behavior, Maxwell-type integrability, Carnot-style efficiency bounds, nonlinear regime separation, and structural break sensitivity to protocol events. Bitcoin's log-temperature behaves as a persistent mean-reverting process with an AR(1) coefficient of 0.97 and a half-life of 21 days; Ethereum is highly persistent, with weaker formal evidence of stationarity than Bitcoin. Maxwell integrability is frequency-dependent: Bitcoin passes all four relations at monthly frequency, whereas Ethereum passes two of four. Carnot-style evidence is the strongest: realized fee extraction efficiency stays well below the implied bound, with daily compliance exceeding 97% on both chains. Structural breaks around Bitcoin ordinals, EIP-1559, the merge, and Shanghai confirm that protocol changes reorganize the temperature relation. The thermodynamic framework provides structure that standard fee market analysis does not, including a first principles efficiency bound and a state space coherence test. The findings provide partial, frequency-dependent, and chain-specific empirical support for a Landauer-based thermodynamic description of blockspace markets.
Mohd Sameen Chishti, Damilare Peter Oyinloye, Jingyue Li
Cross-chain NFT migration refers to the process of transferring digital assets along with their associated functionalities and guarantees between distinct blockchain platforms. However, architectural divergences among these platforms introduce critical challenges, often resulting in features that fail to behave as intended. While protocol-level mechanisms can coordinate data transfer, they are insufficient to resolve deeper compatibility issues arising from fundamental differences in state organization, transaction execution, and ownership representation. Thus, the critical challenge lies in predicting which NFT features can be preserved, which require redesign, and which are fundamentally incompatible, prior to undertaking costly migration attempts. To address this challenge, we first derive a tailored four-layer NFT architecture based on standard blockchain stacks, distinguishing cryptographic, state-management, transaction-processing, and ownership primitives, with explicit upward dependencies. Building on this architecture, we conceptualize an NFT as a bundle of features and define successful cross-chain NFT migration as the preservation of these features. Grounded in this model, we propose a four-phase migration analysis methodology comprising source feature specification, primitive-level dependency mapping, target platform profiling, and compatibility assessment, which classifies each feature as natively preserved, partially mismatched, or completely mismatched. We evaluate this methodology through a proof-of-concept analysis of Ethereum-to-Solana NFT migration, identifying several incompatibility issues that hinder seamless NFT migration.
In the digital era, personal data is continuously collected, processed, and shared by organizations across various sectors. Traditional consent management systems suffer from centralization, opacity, and insufficient user control, making it difficult for individuals to track and enforce their data-sharing preferences. This paper presents ConsentChain, a decentralized consent governance protocol built on the Polygon blockchain. The system leverages Ethereum-compatible smart contracts to implement purpose-bound, time-limited, and user-revocable consent records, backed by an immutable on-chain audit trail. The architecture employs two core Solidity smart contracts—ConsentManager and AccessController—supported by a React-based frontend and MetaMask wallet integration. Comprehensive end-to-end testing demonstrates 18 of 18 test cases passing, validating the correctness of consent lifecycle management, access validation, role-based access control, and event logging. ConsentChain demonstrates that blockchain technology can provide a transparent, tamper-proof, and user-sovereign alternative to conventional consent management systems, with clear pathways toward enterprise adoption, multi-chain deployment, and zero-knowledge privacy extensions. Index Terms—Blockchain, Consent Management, Smart Contracts, Data Privacy, GDPR, Decentralized Systems, Ethereum, Polygon, Access Control, Audit Trail.
Đức An Nguyễn, Minh Thành Phí, Duc Dung Pham, Thị Hương Thơm Hồ
Ngành logistics hiện đại đang đối mặt với các thách thức về sự thiếu minh bạch, chi phí trung gian cao và khó khăn trong việc giải quyết tranh chấp khi giao hàng trễ hạn. Nghiên cứu này đề xuất và triển khai một ứng dụng phi tập trung (Decentralized Application - DApp) quản lý hợp đồng logistics trên nền tảng Blockchain Ethereum (mạng thử nghiệm Sepolia). Hệ thống sử dụng kiến trúc lai (Hybrid Architecture) kết hợp giữa Blockchain để xử lý thanh toán hoặc logic nghiệp vụ và IPFS để lưu trữ hồ sơ phi tập trung. Điểm mới của nghiên cứu là việc tích hợp cơ chế "Ký quỹ thông minh" (Smart Escrow) và "Phạt vi phạm tự động" (Automated Penalty) ngay trong hợp đồng thông minh (Smart Contract). Kết quả thực nghiệm cho thấy hệ thống đảm bảo tính toàn vẹn dữ liệu, tự động hóa hoàn toàn quy trình thanh toán dựa trên trạng thái giao hàng thực tế và xử lý chính xác các trường hợp vi phạm thời gian cam kết mà không cần sự can thiệp của bên thứ ba.AbstractThe modern logistics industry faces challenges regarding lack of transparency, high intermediary costs, and difficulties in resolving disputes over late deliveries. This study proposes and implements a decentralized application (DApp) for logistics contract management on the Ethereum Blockchain (Sepolia testnet). The system employs a Hybrid Architecture combining Blockchain for payment or business logic processing and IPFS for decentralized record storage. A novel feature of this research is the integration of "Smart Escrow" and "Automated Penalty" mechanisms directly within the Smart Contract. Experimental results demonstrate that the system ensures data integrity, fully automates the payment process based on actual delivery status, and accurately handles delivery deadline violations without third-party intervention.Keywords: Blockchain, Ethereum, smart contract, IPFS, Logistics, automated Penalty.
Запропоновано середовище імітаційного моделювання явища максимально екстрактованої вигоди MEV (англ. Maximal Extractable Value), реалізоване мовою програмування Python із використанням бібліотеки Gymnasium, яке відтворює взаємодію сховища-мемпулу, конструювальника блоків, агента MEV-екстрактора та AMM-пулу децентралізованої біржі. Формально середовище описано як розширений та частково спостережуваний процес прийняття рішень, у межах якого агент взаємодіє з дискретно-часовою моделлю епізодів, що відображає послідовність надходження транзакцій, побудови блоків і виконання swap-операцій обміну на децентралізованій крипто-біржі. Для моделювання адаптивної поведінки агента використано методи навчання з підкріпленням, а для кількісного аналізу втрат користувачів застосовано контрфактичний підхід до оцінювання, що дає змогу порівнювати результати виконання транзакцій у різних режимах впорядкування за однакових вхідних умов. У дослідженні використано раніше описаний авторами метод зменшення негативних ефектів MEV-екстракції на основі логічних часових міток Лампорта, який реалізує локальне причинно-наслідкове впорядкування транзакцій у межах окремого смарт-контракту без модифікації глобального механізму консенсусу мережі блокчейн Ethereum. Для оцінювання практичної ефективності цього підходу сформовано три сценарії моделювання: базовий сценарій без систематичної MEV-атаки для визначення накладних витрат застосування механізму захисту, сценарій систематичної sandwich-атаки для аналізу та здатності методу зменшувати втрати користувачів та обмежувати можливості MEV-екстрактора, а також сценарій параметричного аналізу, спрямований на дослідження компромісу між рівнем захисту та "вартістю" його застосування. Отримані результати показали, що запропонований метод MEV-захищеного впорядкування може зменшувати цінові втрати користувачів від sandwich-атак і, водночас, впливати на частоту відхилення транзакцій та пов'язані комісійні витрати, що вказує на наявність керованого компромісу між ефективністю захисту та накладними витратами його використання. Практична цінність роботи полягає у створенні відтворюваного середовища імітаційного моделювання для дослідження стратегічної поведінки MEV-агентів і перевірки механізмів зменшення негативних наслідків MEV у контрольованих умовах, що може бути використано для подальшого аналізу безпеки протоколів децентралізованих фінансів та проєктування нових методів впорядкування транзакцій.
A decentralized system for academic credential verification using Ethereum blockchain and hybrid off-chain storage. The system replaces traditional manual verification by allowing institutions to issue digitally signed certificates whose cryptographic hashes are stored on-chain, ensuring immutability and tamper resistance. A dual-hashing approach (SHA-256 followed by Keccak-256) is used to enhance security and maintain compatibility with the Ethereum ecosystem. Credential files are stored off-chain (e.g., Supabase/IPFS) to reduce cost, while verification is performed by comparing hashes, achieving fast (under a few seconds) and reliable authentication. Overall, the paper demonstrates a scalable, secure, and efficient solution for real-world use cases such as academic admissions and recruitment.
Rongji Huang, Yifeng Ye, Gerui Wang, Mingchao Wan · 8 authors
Due to regulatory compliance and governance management, modern (permissioned) blockchains require flexible endorsement, which allows the endorsement policy for each contract or state object to be individually defined. To enable flexible endorsement, Hyperledger Fabric employs an execute-order-validate (EOV) paradigm, in which transactions first undergo speculative execution and endorsement, and are only then ordered and validated. Meanwhile, most blockchain systems, including the platform targeted in this work (i.e., ChainMaker), still follow a conflict-free order-execute framework. We argue that the EOV paradigm still faces several limitations, notably high abort rates in high-contention workloads such as those in Decentralized Finance (DeFi). To avoid refactoring our system and better suit DeFi applications, we try to integrate flexible endorsement into the classical order-execute architecture and accordingly propose a new framework. The key challenge is to deterministically remove problematic transactions from an ordered list, while preserving censorship resistance and decentralization for the remaining ones. We instantiate this framework on top of Tendermint, a seminal Byzantine fault-tolerant (BFT) protocol adopted in our system, and thereby propose FlexTender. By elegantly embedding endorsements into consensus, FlexTender incurs no additional messaging overhead in the normal case. Empirical evaluation using an Ethereum USDT workload demonstrates that FlexTender achieves up to $10.6\times$ speedup in throughput over an EOV simulation on the same platform.
Zhuoran Pan, Yue Li (102191), Zhi Guan, Jianbin Hu · 5 authors
The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx}, a high-fidelity benchmark featuring 29,921 single-step and 1,575 multi-step instances meticulously derived from 300 days of real-world Ethereum mainnet traces. Unlike prior works that rely on synthetic instructions, \textsc{Intent2Tx} grounds natural language intents in real-world protocol interactions across 11 categories, including diverse long-tail Decentralized Finance (DeFi) primitives. To enable rigorous evaluation, we propose an execution-aware framework that transcends surface-level text matching by employing differential state analysis on forked mainnet environments. Our extensive evaluation of 16 state-of-the-art LLMs reveals that while scaling and retrieval-augmentation enhance logical consistency and parameter precision, current models struggle with out-of-distribution generalization and multi-step planning. Crucially, our execution-based analysis demonstrates that syntactically valid outputs often fail to achieve intended state transitions, highlighting a significant gap in current "reasoning-to-execution" capabilities. \textsc{Intent2Tx} serves as a critical foundation for developing autonomous, reliable agents in intent-centric Web3 ecosystems. Code and data: https://anonymous.4open.science/r/Intent2Tx_Bench-97FF .
Ermanno Francesco Sannini, Francesco Salzano, Simone Scalabrino, Rocco Oliveto · 7 authors
Smart Contracts are essential blockchain components, mainly written in Solidity. The high availability of public Solidity code leads to frequent reuse and high clone ratios. Since cloning can propagate vulnerabilities and flaws, effective detection is crucial. Although existing techniques work well in detecting syntactic clones, the identification of semantic clones is an open problem. To address this challenge, in this paper, we present and empirically assess a scalable methodology, based on analyzing code and comments, to spot semantically equivalent Solidity functions. We first collected an up-to-date dataset of about 300,000 Ethereum smart contracts, 82.07% of which are compliant with modern Solidity version 0.8. Manual validation of a statistically significant sample comprising 1,155 function pairs confirms the effectiveness of our solution, achieving an overall precision of 59% (rising to 84% for homonymous functions) and a recall of 97%. Besides, we explore the structural differences occurring on semantically equivalent Solidity functions, demonstrating that they often represent design alternatives focused on security choices, modularization, and gas optimization. Finally, we investigate the use of Large Language Models (LLMs) as documentation engines in scenarios where code comments are poor or absent. Our results show that LLM-generated summaries, combined with sentence transformers like BERT, can bridge the documentation gap, enabling the identification of semantic clones in uncommented code with 75% precision. This work establishes a modern benchmark for Solidity clone detection and provides a foundation for the automated discovery of secure and efficient code alternatives.
Code-driven auditing fails when correctness depends on what the specification requires rather than how the code is written. Production blockchain networks expose this directly: byzantine consensus runs many independent clients of a shared specification, so a specification-divergence defect in one client can fork the network or halt finality. Existing tools reason one repository at a time, with no shared baseline held constant across implementations. We present SPECA, an LLM-driven audit framework that derives explicit, categorized security properties (invariants, pre/postconditions, trust assumptions) from natural-language specifications and reuses them across implementations. SPECA enables controlled cross-implementation comparison, detections grounded in specification invariants no code pattern encodes, and false positives traceable to a specific pipeline phase rather than opaque model errors. On the Sherlock Ethereum Fusaka Audit Contest (10 targets, 366 submissions), SPECA recovers all 15 in-scope H/M/L vulnerabilities expert-augmented (8/15 automated-only) and surfaces 4 fix-confirmed bugs, including a cryptographic-invariant violation missed by every adjudicated finding. On the RepoAudit C/C++ benchmark, SPECA reaches 88.9% precision at 100% recall (F1=0.94) and surfaces 12 author-validated bugs beyond ground truth, two externally validated. SPECA also flags 5 of RepoAudit's 40 published bugs as defensive-coding fixes with no reachable exploit path. False positives trace to three pipeline-pinned root causes; a multi-model study identifies property-generation quality as the binding constraint. End-to-end cost is ~$30 per H/M/L bug (~42 min wall-clock under parallel execution).
The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p &amp;lt; 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.
Interoperability of patient files between hospitals continues to present significant obstacles. Health systems frequently utilize central EHR systems that could suffer malfunctions, data breaches, and unauthorized access by third parties. Not only does this jeopardize patient confidentiality, but it also hinders the efficient operations of hospital processes.Blockchain technology is viewed as a prospective remedy for the issue. Blockchain keeps its data differently, allowing users to store data securely and make changes difficult. In this study, we analyze research works published between 2016 and 2023 regarding blockchain-based hospital-to-hospital data exchange.The methodologies differ widely: there are cases where researchers use smart contracts in Ethereum, build a system on Hyperledger Fabric, and deploy IPFS. Moreover, certain studies incorporate encryption methods, machine learning algorithms, and more. In summary, the results show that blockchain allows for improved data protection and transparency while giving patients more control over their personal information. Still, some issues persist, such as scalability, expenses, integration with existing infrastructure, and adherence to GDPR and HIPAA requirements. For future work, more improvements are necessary. For instance, zero-knowledge proofs, cybersecurity measures for new technologies, and using artificial intelligence to audit and validate smart contracts may be promising solutions.
Penelitian ini mengevaluasi efektivitas federated learning dalam mendeteksi alamat ilegal pada blockchain Ethereum untuk Anti Money Laundering (AML). Studi ini membandingkan XGBoost centralized dan federated dalam simulasi multi exchange, data dibagi secara horizontal menjadi 3 subset yang merepresentasikan entitas bursa independen. Untuk mengisolasi pengaruh pelatihan terdistribusi, pembagian data dilakukan secara homogen (IID), sehingga analisis difokuskan pada perbedaan mekanisme pembelajaran tanpa dipengaruhi heterogenitas data. Hasil menunjukkan bahwa model federated mencapai performa yang kompetitif dengan PR AUC 0,9962 dan akurasi 97,11%, dibandingkan model terpusat dengan PR AUC 0,9975 dan akurasi 97,75%. Namun, performa tersebut disertai peningkatan durasi pelatihan 4,01 detik dibandingkan 3,39 detik, yang disebabkan oleh beban komunikasi selama proses pembaruan model. Temuan ini menegaskan adanya trade off antara kinerja dan efisiensi dalam penerapan federated learning. Meskipun mampu mendekati performa pembelajaran centralized, pendekatan ini menimbulkan biaya operasional tambahan. Studi ini merupakan evaluasi dasar dalam kondisi terkontrol dan belum merepresentasikan skenario dunia nyata, sehingga diperlukan penelitian lanjutan pada data non IID dan skala sistem yang lebih besar.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governing entity that empowers its stakeholders (i.e., users who hold one or more of its tokens) to manage blockchain-based protocols (i.e., smart contracts) collaboratively. The governance of a DAO is explicitly encoded in the DAO's governance contract, which defines how stakeholders participate in governance and how much influence (or voting power) they have in any decision. While decentralization and autonomy are the fundamental tenets of a DAO's design, empirical evidence suggests that in practice governance is often highly centralized. In this work, we study the designs and implementations of 48 public and actively used DAOs, with substantially large capital, deployed on Ethereum. We identify how three key governance mechanisms--token registration, staking, and delegation--originally introduced to improve security or participation, contribute to the concentration of voting power. Unlike prior work on centralization of voting power in specific DAOs, our findings reveal that these governance mechanisms of DAOs themselves systematically reinforce centralization. By elucidating the relationship between governance design and voting centralization, this work advances the understanding of DAO governance structures and highlights the inherent trade-offs between decentralization, security, and usability of DAOs.
Aman Chaudhary, Bhavy Singhal, Aryan Siwach, Priyanka Dhanraj
Abstract The rapid increase in digital data has led to heavy reliance on centralized cloud computing. Consequently, users are exposed to critical vulnerabilities, including unauthorized access, privacy invasion, and single points of failure. This study proposes a cloud storage system that is trustless to address these challenges that have persisted. The underlying methodology utilizes distributed data hosting based on the InterPlanetary File System (IPFS) and decentralized access control through Solidity smart contracts. Under this architecture, file metadata is stored safely on an unalterable blockchain registry, and the media files are stored off-chain. These contracts are automatically run by granular access controls like specific public and private visibility modes. At any point, no outside intervention of a third party is needed. The system was checked during the testing time in terms of a functional accuracy in regards to a secure storage, verifiable retrieval, and instant revocation of permissions. According to the key results, the elimination of intermediary control, prevention of unauthorized access to data attempts, and high data availability are achieved. In conclusion, this shows that a combination of programmable smart contracts and peer-to-peer storage will provide a potentially scalable and secure alternative to the traditional cloud architecture. This leads to a considerable improvement in user data sovereignty and systemic resilience as a whole. Keywords Ethereum, Solidity, IPFS, Smart Contracts, Decentralized Storage