Search Results (155)

Blockchain-based sensor networks rely on public-key cryptography for transaction verification, auditability, and data integrity. However, widely used public-key mechanisms are quantum-vulnerable in the presence of Cryptographically Relevant Quantum Computers (CRQCs), requiring sensor-to-blockchain transactions to address both post-quantum security and exposure control. This paper proposes a post-quantum sensor-to-blockchain transaction framework that minimizes CRQC-aware exposure while preserving low-cost auditability. It defines a transaction workflow that represents sensor data through hash-based commitments instead of storing raw measurements on-chain. The workflow combines Module-Lattice-Based Digital Signature Algorithm (ML-DSA)-based authentication, threshold-based authorization, Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM)-protected relay communication, and an event-based smart contract (EBSC) for compact audit recording. A Quantum Exposure Score (QES) is introduced as a transaction-level metric to quantify CRQC-induced exposure across cryptographic, relay, key-lifecycle, migration-readiness, and authorization dimensions. The framework is evaluated using differential pulse voltammetry (DPV) electrochemical sensor data, Constrained Application Protocol (CoAP) communication, and a Ganache-based blockchain, with scalability runs of up to 10,000 sensor transactions and ablation baselines. Compared with full on-chain storage, EBSC reduces gas consumption by approximately 80%, while QES decreases from 100 in the classical open scenario to 4 in the full framework. These results demonstrate that the proposed design provides a practical path for post-quantum secure sensor-to-blockchain transactions. Full article

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

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

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

Digital evidence management systems are designed to ensure that the digital evidence is genuine and effectively handle its complexity. In this work, blockchain technology is applied to handle the digital evidence by introducing several layers of security to ensure its protection, data integrity, and confidentiality, as well as trace the evidence throughout all its phases. To store the evidence files and their metadata, the proposed system uses a decentralized storage architecture that utilizes the InterPlanetary File System (IPFS) and Google Drive. Moreover, the proposed system ensures the chain of custody of the digital evidence through the use of Hyperledger Fabric technology. In addition, smart contracts (chaincode) are used in this work to validate the digital evidence, enforce strong access controls, and protect evidence metadata integrity. To ensure reliable transaction sequencing and consistency across the distributed ledger, an ordering service is used. At last, we combine two hash algorithms, symmetric encryption, file fragmentation, and metadata logging to protect the digital evidence from unauthorized access. The proposed framework is integrated with modern forensic tools, including Autopsy. The procedure of acquiring and analyzing digital evidence is made straightforward by the application of a set of forensic procedures. Moreover, the system’s modular design allows users to perform preprocessing operations, administer the decentralized storage, administer the evidence retrieval, test system performance, and enhance the system scalability. Moreover, we implemented secure coding practices and applied large language models to mitigate identified vulnerabilities, including weak system input validation, concurrent access to the system, and an insecure logging system. The experimental results indicate that the proposed framework preserves the digital evidence’s integrity, ensures chain of custody, and records all transactions. Results also indicate that the digital evidence is protected from unauthorized access and change attempts. Finally, by following local relevant regulations and established standards, the digital evidence should be admissible in court. Full article

Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Smart contract auditing remains challenging because vulnerabilities often emerge only under complex execution conditions, cross-transaction interactions, and environment-dependent assumptions. Existing analysis techniques, including static analysis, symbolic execution, fuzzing, and recent LLM-assisted approaches, each provide useful but incomplete coverage, and monolithic auditing pipelines often struggle to balance search breadth, reproducibility, and reporting reliability. This paper presents SEMA, a self-evolving multi-agent auditing framework for smart contracts that formulates auditing as a resource-bounded discovery of concrete counterexamples under replay-certified reporting semantics. SEMA combines heterogeneous specialized agents, an orchestrator, a shared artifact-centric knowledge base, and a replay-based referee. During auditing, agents generate and consume reusable artifacts, such as candidate invariants, refuted hypotheses, transaction templates, and coverage cues, allowing the shared search state to evolve across rounds without modifying the analyzers themselves. To ensure reporting reliability, findings are accepted only when the referee can replay the candidate scenario under a pinned execution configuration and confirm violation of an executable security property. We further evaluate SEMA on an annotated smart contract benchmark under a fixed 300 s budget per contract. The full system achieves 0.9469 instance recall, 0.9441 success rate, and 0.9445 macro-average category recall on the retained executable subset, outperforming both symbolic-only and fuzzing-only baselines, as well as multi-agent ablations that disable dynamic knowledge evolution or cross-agent artifact reuse. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

Threat modelling is a methodology employed for identifying and analysing threats and applicable mitigations for web applications, mobile applications, infrastructure, and environments including smart home environments. Threat modelling starts with a tabletop exercise to identify threats. It provides extremely important insights into what can go wrong if certain events or a series of events take place. The identification of these events is critical to ensuring the right mitigation strategies are applied. Threat modelling also helps to identify security controls that may be assumed to provide required security, but, in reality, may not be addressing the existing and applicable threat(s). Existing literature, in the public domain and in academia, discusses threat materialisation for smart homes; however, entry points for a threat to materialise and exploit these vulnerabilities are not explored and a dedicated threat model for smart home environments is currently unavailable. Whilst threats can be mitigated by smart home device manufacturers, there are also mitigations that need to be applied by smart home owners who are both technology-aware and technology-unaware. In this paper, we propose a structured, domain-specific threat modelling methodology for smart home environments. The methodology models threats from a smart home owner’s perspective, identifies entry points and the mitigations that need to be implemented by a smart home owner. It also acknowledges that the attack surface expands and contracts and is not constant; which is addressed by applying zero-trust principles. Full article

This article proposes a mathematical model for quantitatively assessing the cyber resilience of modern blockchain platforms. Constructing the mathematical model, the authors proposed representing the architecture of a blockchain-based information system as four layers: the cryptographic algorithm layer, the blockchain core layer, the smart contract layer, and the decentralized application layer. A study of typical vulnerabilities was conducted for each layer, and a list of countermeasures to counter potential threats was proposed. Then, key elements and their impact on the system’s cyber resilience were identified. As a result, a mathematical model for assessing the cyber resilience of blockchain platforms was developed. Based on the analysis of the model, it was experimentally demonstrated that a cyber attack carried out at a lower layer affects all higher layers of the blockchain platform, and cyber resilience at the current layer can only be effectively ensured if it is ensured at the previous layer. Full article

Infectious disease outbreaks continue to pose a significant threat to global health, underscoring the importance of timely detection and reliable reporting for effective interventions. Traditional reporting systems often rely on hierarchical data flows, which introduce delays, inconsistencies, and vulnerabilities, as highlighted during the COVID-19 pandemic. Blockchain, a disruptive technology, offers a promising solution. This study proposes a blockchain-based infectious disease reporting system built on Hyperledger Fabric that supports multi-level reporting and governance across national health systems. The architecture preserves hierarchical structures while enabling real-time reporting across authorized health stakeholders. It separates public test results from sensitive patient information, with private data secured via Private Data Collections and anchored using cryptographic hashes. Smart contracts enforce role-based access and validation, ensuring data integrity and controlled oversight. The system prototype was deployed within Docker containers and evaluated using illustrative COVID-19 case data. Network performance was benchmarked using Hyperledger Caliper, measuring throughput, latency, and resource utilization. The results demonstrate proper system functioning and stable transaction processing under the tested experimental conditions, supporting the feasibility of the proposed architecture for privacy-preserving multi-level infectious disease reporting systems. Full article

Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings. Full article

The rapid convergence of the Internet of Things (IoT) and decentralized finance (DeFi) is reshaping the digital economy by enabling autonomous, trustless, and value-driven interactions among connected devices. This paper provides a comprehensive survey of the emerging paradigm that combines IoT’s pervasive sensing and communication capabilities with DeFi’s programmable financial infrastructure. We first discuss the motivation behind this convergence and explore key opportunities, including autonomous machine-to-machine (M2M) payments, decentralized data marketplaces, and trustless IoT service provisioning. Despite its potential, IoT–DeFi integration introduces significant security and privacy challenges related to smart contract vulnerabilities, consensus protocol risks, oracle manipulation, and constrained device capabilities. We review existing mitigation approaches such as lightweight cryptography, secure contract design, and decentralized identity management, and critically assess their limitations in heterogeneous, resource-limited environments. Building on this analysis, identify research gaps and propose future directions emphasizing formal verification of IoT-integrated smart contracts, robust oracle design, interoperability frameworks, and privacy-preserving trust models. This survey systematically maps opportunities, threats, and open issues. In doing so, it guides researchers and practitioners toward building secure, scalable, and energy-efficient IoT–DeFi ecosystems for next-generation decentralized applications. Full article