Search Results (1,466)

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) have exhibited certain potential in vulnerability detection and explanation, the coarse-grained modeling of traditional binary preference optimization paradigms hinders the model ability to learn the priority of domain-specific requirements, frequently leading to extreme optimization at the cost of detection accuracy. Furthermore, existing approaches fail to consider non-ideal factors in real-world application scenarios and overlook noise interference induced by missing prompts, which results in inadequate detection stability and reliability, making them challenging to adapt to complex practical scenarios. To address these critical issues, this study proposes a Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection (NAPO-SCVD). This method adopts a four-stage framework consisting of data construction, continuous pre-training, supervised fine-tuning, and noise-aware preference optimization. Specifically, it enhances the model’s comprehension of contract syntax and semantics through domain-specific pre-training, improves its detection and explanation capabilities using high-quality datasets, constructs deliberately guided biased explanations to simulate noisy samples, refines preference gradients, and strengthens the model’s anti-interference ability. Consequently, this approach achieves high-precision and high-reliability smart contract vulnerability detection, along with fine-grained explanations. Full article

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

Small- and medium-sized enterprises, especially in the agricultural food sector, struggle to implement end-to-end product traceability systems, such as enterprise resource planning (ERP), due to the high costs and complexity involved for businesses of this scale. As customer expectations and regulatory requirements place an increasing emphasis on traceability and transparency, the combined use of industrial Internet of things (IIoT) technologies and blockchain-based smart contracts offers a promising pathway to cost-effective automation of supply chain processes. This paper develops a conceptual, multi-layer architecture that integrates sensing, communication, integration and smart-contract layers to support affordable, automated and extensible traceability for agri-food supply chains. Building on information processing theory and transaction cost economics, the framework explains how such architecture can reduce information uncertainty, lower monitoring costs and strengthen the organisational trust in agri-food supply chains. The framework is empirically illustrated and tested through an implementation that links distributed sensing infrastructure with a blockchain-based smart contract in a real agricultural supply chain setting. The evaluation assesses operational performance, data integrity and cost-efficiency, demonstrating that the proposed architecture can serve as a viable alternative or most importantly complement to traditional ERP solutions for small- and medium-sized enterprises that seek end-to-end traceability, transparency and automation. Full article

In smallholder-dominated agricultural systems, farm expansion is often expected to improve agricultural performance, yet a larger operated area does not necessarily translate into higher returns per unit of land. This issue is particularly relevant in rice farming, where land fragmentation, labor constraints, and uneven access to agricultural services may limit the return-enhancing effect of farm-scale management (FSM). Using 2024 household survey data from 732 rice-farming households in Jiangxi Province, China, this study examines how FSM, service-scale management (SSM), and their organizational matching affect rice-farming returns (RFR). We apply ordinary least squares (OLS) regression models with interaction-term specifications and further conduct mechanism, moderation, and heterogeneity analyses. The results show that FSM alone does not automatically increase per-mu net operating returns, whereas SSM is positively associated with RFR. More importantly, the interaction between FSM and SSM is significantly positive, indicating that farm expansion generates return advantages mainly when supported by agricultural socialized services. Mechanism analysis suggests that this synergistic effect operates partly through higher land consolidation (LC) and more formalized service contractualization (SC), while smart agricultural technology (SAT) further strengthens the return-enhancing effect. Heterogeneity analysis further shows that the effect differs across farmers with different operating scales. These findings suggest that smallholder modernization should not be understood as land expansion alone but as the organizational matching between farm scale and the service-based division of labor. Policy efforts should therefore aim to improve agricultural socialized service systems, promote land consolidation, strengthen service contracts, and integrate smart agricultural technologies into service provision. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

This work presents a comprehensive study of entropy-based metrics for evaluating blockchain systems, focusing on on-chain ledger immutability, off-chain data integrity, and computational dynamics within blockchain virtual machines (BVMs). We develop a unified framework that models blockchain states as probabilistic distributions, quantifying uncertainty through Shannon entropy and examining its evolution under varying adversarial fractions. Extensive simulations demonstrate that on-chain entropy exhibits near-exponential decay, reflecting the cumulative reinforcement of honest consensus, while off-chain entropy remains static, highlighting the limitations of conventional data storage. Furthermore, the BVM is analyzed in terms of computation entropy, establishing its Turing completeness and demonstrating that smart-contract state evolution mirrors the information dynamics of arbitrary Turing machines. Our results provide quantitative evidence that entropy serves as both a theoretical and operational measure of immutability, tamper evidence, and protocol resilience. The proposed entropy framework offers practical tools for monitoring ledger integrity, detecting tampering, and assessing computational complexity, bridging the gap between information-theoretic principles and distributed ledger applications. This study advances both the theoretical understanding and practical evaluation of blockchain security, providing a principled methodology for analyzing distributed systems under adversarial conditions. Full article

Faculty governance in higher education depends on transparent participation, reliable quorum enforcement, accountable record keeping, and strict alignment with institutional regulations. Conventional departmental council processes provide formal authority and academic deliberation, but they often rely on manual documentation, fragmented records, and procedural enforcement that is difficult to verify after the fact. This work presents an integrated hybrid Decentralized Autonomous Organization (DAO) framework for faculty governance that combines regulatory alignment analysis, a working smart-contract prototype, and scenario-based simulation. The framework is designed for university departmental councils and is structured across three layers: off-chain community governance, on-chain protocol governance, and off-chain execution governance. It expands prior conceptual work by incorporating governance dimensions related to roles, incentives, membership, communication, decision-making, identity, auditability, conflict-of-interest handling, and institutional ratification. The evaluation simulates 1488 proposals across twelve scenarios covering four faculty sizes (15, 30, 50, and 100 members) and three adoption levels (low, moderate, and high). Scenario results indicate that adoption intensity is the dominant driver of governance performance: mean participation increases from about 33% under low usage to about 85% under high usage, quorum achievement rises from about 6% to about 96%, and execution rises from about 19% to about 70%. Relative to a modeled conventional workflow baseline, the DAO-supported process reduces decision-cycle time by about 76%, improves audit completeness by about 30%, and increases traceability from about 0.63 to 1.00. The results indicate that DAO-assisted faculty governance can strengthen transparency, procedural consistency, and auditability while preserving legally mandated university authority, but its practical value depends on sustained participation, privacy safeguards, cost control, and clearly defined hybrid control points. Full article

A blockchain-based framework is proposed for secure vehicle registration and real-time authenticity verification in vehicular networks. To mitigate the risks of fake and stolen license plates, vehicle identification data is protected using a modular arithmetic-based cryptographic mechanism and indexed within an on-chain hash table structure. Role-based access control ensures system integrity by restricting all registration and modification operations to authorized government entities, while enabling public verifiers to validate vehicle legitimacy through privacy-preserving verification. Experimental evaluation demonstrates that the system achieves low verification latency, minimal storage overhead, and stable throughput. Furthermore, scalability and denial-of-service (DoS) resilience analyses confirm consistent performance under high verification demand. This framework offers an efficient and privacy-preserving solution for the secure and real-time verification of vehicle legitimacy in vehicular networks. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Agricultural cooperatives in developing economies struggle with capital access and typically depend on subsidized credit with rigid repayment schedules that create vulnerability during low-production cycles. In this paper, we present a mathematical framework implemented through a smart contract to tokenize cooperative capital. Our mathematical framework uses magnified accumulators (scaled accumulator variables) to maintain temporal fairness, allocating dividends proportionally based on token holding periods through correction factors. The dividend distribution model operates with $O\left(1\right)$ computational complexity, regardless of cooperative size. The CooperativeToken smart contract combines ERC20 standards with automated dividend distribution, democratic governance mechanisms, and a hybrid payment architecture supporting both cryptocurrency and fiat transactions. Deployment verification and a gas analysis demonstrate operational viability with consistent performance and minimal transaction costs, enabling scalability from small to large cooperatives. The proposed system offers agricultural cooperatives a debt-free alternative to conventional financing, democratizing access to tokenized capital structures that were previously restricted to large agribusinesses. While the model is validated via Ethereum Sepolia testnet simulation, real-world deployment and field testing in active cooperatives remain necessary to confirm practical feasibility. This study provides the algorithmic and economic foundation for such pilots. Full article

Successful project delivery largely depends on effective progress management to ensure schedule reliability and resource efficiency. Conventional manual and paper-based approaches remain inefficient and error-prone, often causing fragmented data and poor collaboration among stakeholders. To overcome these limitations, this study proposes a computer vision–blockchain integrated framework for closed-loop construction progress management within the Plan–Do–Check–Act (PDCA) cycle. This system supports an automated, end-to-end workflow in which UAV-captured images are processed by a computer vision model, digitally signed, and verified on a blockchain ledger, triggering smart contract-based schedule deviation alerts to relevant stakeholders. An enhanced digital signature scheme ensures data integrity during off-chain and on-chain transitions, while self-executing smart contracts coordinate schedule submissions, progress reporting, and deviation detection. Implemented on Hyperledger Fabric and validated through a case study, the framework demonstrates transparent data flow and strong performance in detection accuracy, latency, and throughput. By shifting progress management from passive reporting toward proactive control, this study provides a replicable, transparent, and tamper-resistant solution for multi-stakeholder construction progress governance. Full article

To support Industry 5.0 smart factories that require ultra-low latency and high reliability, this paper proposes a three-layer Intent-Based Zero-Touch Networking (IBZTN) pipeline. Existing Intent-Based Networking (IBN)/Zero-Touch Networking (ZTN) studies often remain conceptual, while Graph Neural Network (GNN)-based Quality of Service (QoS) prediction and Deep Reinforcement Learning (DRL)-based reconfiguration are usually developed as separate modules. The proposed pipeline connects natural-language intent translation, feasibility prediction, and minimal-modification reconfiguration through a validated QoS contract and feasibility-aware closed-loop structure. Layer 1 converts intents into quantitative QoS profiles by combining Retrieval-Augmented Generation (RAG) with schema- and rule-based validation. Layer 2 evaluates feasibility using a Graph Isomorphism Network with Edge features (GINE)-based binary classifier. Layer 3 recovers infeasible states using a Behavior Cloning (BC) Proximal Policy Optimization (PPO) agent with Smart Traffic Engineering (TE) masking. In experiments with 300 natural language intents, RAG+Validator reduced Layer 1 constraint violations to 0.0% for most evaluated cloud and local Large Language Models (LLMs). The Layer 2 predictor achieved a 93.9% F1-score, and Layer 3 achieved an 87.8% recovery success rate with 9.8 average modifications and 5.56 ms inference latency. These results demonstrate the simulation-level potential of IBZTN and motivate future hardware-in-the-loop validation in industrial networks. Full article

As digitalization is widely used, Industrial Control Systems (ICSs) face severe cybersecurity challenges, where traditional defenses often lack real-time detection and immutable audit trails. Therefore, we propose a security alert platform that integrates blockchain, smart contracts, and homomorphic encryption. By leveraging the decentralized architecture of blockchain, the platform ensures the integrity and non-repudiation of operational logs. Concurrently, anomaly detection logic is embedded within smart contracts to enable an automated, real-time alerting mechanism. Furthermore, to preserve industrial data privacy, homomorphic encryption is employed, allowing the system to perform anomaly detection directly on encrypted data, thereby maintaining confidentiality throughout the data lifecycle. Preliminary analysis indicates that the proposed platform effectively enhances the resilience of ICS, strengthening both defense against unauthorized operations and post-incident forensic capabilities. Full article