<p>The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.</p>

MKDD-Vul: A lightweight multi-modal knowledge distillation framework for detecting vulnerabilities in smart contracts

SmartScope: Smart contract vulnerability detection via heterogeneous graph embedding with local semantic enhancement

The other issue that is still of challenge in Non-Governmental Organization (NGO) fund management systems concerns transparency and accountability. The conventional centralized systems do not give donors visibility and can be subjected to data distortion. This paper suggests a decentralized blockchain based system of transparent management of NGO funds in the Ethereum smart contracts and MERN stack in order to solve these problems. The system will guarantee a write only history of transactions, automatic donation management, and live monitoring of donors. Coded smart contract records are written in Solidity and support donations, whereas MetaMask can be used to support authenticated blockchain transactions. With the proposed platform, it will remove intermediaries, prevent tampering, and increase the donor trust due to end-to-end transparency. The system is experimentally validated on test networks on the Ethereum platform, and shows it to be secure, reliable, and scalable.

It is a medical research project that has a secure and privacy-preserving architecture through blockchain hashing and encrypted AI pipelines. Conventional healthcare data systems are vulnerable to the risks of centralization and data manipulation as well as poor privacy provisions hence cannot be trusted in delicate medical investigations. The suggested system would guarantee the integrity of data through the storage of cryptographic hashes of anonymized medical records in a blockchain where tamper-evident traceability is ensured. At the same time, patient data is stored off-chain and encrypted, safeguarding the identity and retaining the analytical value. The access control is controlled by a smart contract layer where only verified studies can access approved datasets. Medical records are verified by physicians before being included to make the data valid and authentic. The AI models are produced using encrypted and anonymized data to generate research insights without the need to disclose personal data. The architecture provides a solid basis of ethical, transparent, and scalable medical data use that would protect both privacy compliance and reliability of research in future systems based on digital healthcare.

Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements.

Blockchain and smart contracts are bringing a technological transformation to banking and financial service industry. This scholarly article evaluates the revolutionary nature of smart contracts in reinventing the concepts of trust, efficiency, and automation in transactions of diverse financial sectors. By using a qualitative and explorative methodology that uses secondary resources, the research integrates the know-how of academic publications, white papers, policy-related pieces, and case studies published since the year 2020. The results show that smart contracts are increasingly being used in trade finance, cross border payment, insurance claim settlement, credit release as well as compliance with regulations. Such applications have resulted in cost efficiency, transparency, auditability, and speed of operation being strengthened tremendously. Nevertheless, the paper also reveals some of the existing problems such as the lack of legal clarity, weaknesses in the coding of contracts, scalability of the blockchain technology used, regulatory compliance, and privacy. In practice, being used by institutions like JPMorgan and Santander and in DeFi platforms like Aave and Compound, smart contracts are increasingly becoming institutionally friendly. Also, legal and compliance agencies in different jurisdictions, such as European Union, India and United States, are developing infantile legal regimes that plan to control such innovations. This paper provides the conclusion that smart contracts have a potential to become the backbone of an automated, decentralized, and trusted financial world. To achieve successful integration, there must be a coordination between regulators, technologists, the financial institutions, and policymakers. The paper adds value to the academic discussion by offering a clear, detailed, practice-oriented view on the topic of how smart contract is changing future of banking and finance.

Smart contracts are self-executing programs deployed on blockchain platforms that facilitateautomated and decentralized transactions. However, once deployed, they become immutable, makingthem vulnerable to catastrophic exploits, such as reentrancy, access control misconfiguration, integeroverflow, and front-running. The need for proof and verification is urgent, as evidenced by other highprofile,capital-draining incidents, such as the DAO attack and Parity wallet vulnerabilities. Abstract:We present ContractFuzzer, a systematic fuzzer for detecting vulnerabilities in Ethereum smartcontracts. Existing tools are based on static analysis, symbolic execution, or heuristic detection, andthus typically impose high false positives, low completeness, and limited formal verification. In thispaper, we introduce SmartScan, a formal verification framework that systematically checks smartcontract security by integrating FSM modeling and CTL-based model checking in nuXmv. Ourmethodology performs automatic parsing of Solidity code, automated generation of FSM and BIPmodels, conversion to the SMV format, and verification of CTL security properties. It responds todetected violations with automated counterexample generation to assist in debugging and iterative reverification.For validation, SmartScan will be tested on 10 different types of Solidity contracts thataddress 14 critical vulnerabilities. Our experimental results show 95.4% detection accuracy, 3.2% falsepositive rate, and 2.8% false negative rate, with 100% verification coverage, and average verificationtime of 3–7 seconds for each property, outperforming state-of-the-art tools in both coverage andprecision. SmartScan: SmartScan has a wide-ranging practical utility in discovering and diagnosingvulnerabilities such as reentrancy and access control issues, which it has been applied in, such as in acase study of a DeFi Lending contract. SmartScan provides a scalable, precise, and developer-centricapproach to improve the confidence and reliability of blockchain applications by combining exhaustiveformal verification of smart contracts with automated counterexample generation.

The rapid growth of digital technologies has encouraged organizations to adopt management systems that prioritize transparency, security, and efficiency. Blockchain has emerged as a transformative innovation capable of reshaping conventional management processes through its decentralized and tamper-resistant architecture. This study analyzes the implementation of blockchain in enhancing transparency and security within management systems. A literature review approach was used to examine recent scholarly publications related to blockchain applications across various organizational settings. The findings indicate that blockchain significantly improves data integrity, prevents fraud, and strengthens accountability through distributed ledgers, cryptographic mechanisms, and smart contracts. However, challenges such as scalability limitations, infrastructure readiness, regulatory uncertainties, and limited technical literacy remain major obstacles. This study concludes that blockchain presents substantial benefits, but its effective implementation requires a comprehensive and strategic approach to ensure organizational readiness and long-term sustainability.

A smart contract vulnerability detection method based on deep semantic feature fusion

Novel advances in healthcare-related Internet of Things (IoT) systems have recently had significant impacts on clinical decision-support systems (CDSS) and patient health monitoring. Securing networks using conventional cybersecurity models becomes increasingly challenging as the regularity of open-access networks increases, exposing critical attack regions. The research presented here recommends a hybrid model combining Blockchain (BC) and Intrusion Detection Systems (IDS) built on Deep Learning (DL) (Hybrid BC + DL Model) to address such problems. This method integrates Artificial Intelligence (AI) and distributed trust management (DTM) for providing healthcare-specific security across the entire system. The hypothetical model focuses primarily on a Deep Sparse Autoencoder (DSAE) that helps standardize and reduce all the different traffic generated by medical IoT devices into a small, discrete graphical representation. These embedded technologies were securely encrypted by applying multiple layers of authentication. The primary layer is a standard Bidirectional Long Short-Term Memory (BiLSTM) network that captures temporal dependencies within healthcare data. The next layer is a set of high-powered sensor networks that can detect Distributed Denial of Service (DDoS), Man-in-The-Middle (MiTM), and Brute-Force Attacks (BFA). The simulation test result is subsequently validated using a Bayesian Product-of-Experts (BPoE) method that incorporates contextual medical challenges into the analysis, applies temperature scaling during testing, and improves clinical implementation accuracy. Networks that integrate BC technology have fixed audit logs, Smart Contracts (SC) that automate access control, and Practical Byzantine Fault Tolerance (PBFT) consensus protocols, which permit the secure communication of attack data across the healthcare industry. The proposed model improves conventional benchmarks by 7.39-20.42% and SOTA (State-of-the-Art) by 1.00-7.19%, attaining accuracy scores of 96.73% and 93.58% on the IoT-Flock and the Canadian Institute for Cybersecurity Internet of Things 2023 (CICIoT2023) datasets. In both cases, the detection latency is less than 16 ms, demonstrating real-time feasibility in controlled experimental settings. When training and testing on distinct datasets, the average score ranges from 11.52 to 13.55%, indicating moderate generalization capability as measured by cross-dataset testing. DSAE-based Feature Extraction (FE) generated a 7.28% boost to accuracy, while the Bayesian Fusion Mechanism (BFM) resulted in around a 5.06% boost in accuracy. The outcome results of this study indicate that applying trained models to collected data resulted in a significant 9.39% improvement in accuracy. There was a 7.28% boost in accuracy when DSAE-based Feature Extraction (FE) was applied, 9.39% when algorithms that had been trained were used for collected data, and 5.06% when the Bayesian Fusion Mechanism (BFM) was implemented. The research findings have confirmed all these improvements. The analysis shows that the BC function has a Network Throughput (NT) of more than 698 Times Per Second (TPS), consensus delays of less than 468 ms, and validation success rates of more than 99.4%. Accuracy is maintained above 99.6%, and SC-based security measures are fully operational within 245 ms. The proposed model aims to secure the healthcare system (HCS) and prevent data loss from digital attacks.

This paper explores how Southeast Asia can build the legal foundations to support a “Twin Transition”: the shift toward both digital transformation and green sustainability, through Public-Private Partnerships (PPPs). Given ASEAN’s diverse legal systems, climate risks, and infrastructure gaps, PPPs are key to attracting private investment and innovation. But managing both digital and green goals in long-term contracts requires legal frameworks that are flexible, fair, and adapted to regional realities. This paper details a range of legal tools and mechanisms crucial for navigating this nexus in Southeast Asia. This includes the evolution of performance-based contracts to encompass both digital efficiency metrics and specific environmental outcomes, alongside innovative risk allocation strategies for technological obsolescence, cybersecurity, and climate-related events pertinent to the region. The paper examines the transformative potential of smart contracts and blockchain for enhancing transparency, accountability, and automated compliance in ASEAN’s digital-green projects, while also addressing their inherent vulnerabilities and regulatory challenges across diverse jurisdictions. Furthermore, it delves into the critical role of comprehensive data governance frameworks, including privacy-enhancing technologies and emerging AI governance standards, to manage the vast data generated by smart green infrastructure. Finally, it highlights the importance of fostering adaptive regulatory sandboxes for accelerating innovation, establishing robust dispute resolution mechanisms for complex, integrated projects, and leveraging existing and emerging international legal norms (e.g., cross-border data privacy rules, ASEAN digital integration frameworks) for facilitating greater regional collaboration in the Twin Transition. Ultimately, this paper advocates for a proactive, adaptive, and ethically grounded legal architecture within ASEAN that can effectively bridge the divide between digital innovation and environmental stewardship, enabling PPPs to fully realize their potential as catalysts for a truly sustainable, technologically advanced, and resilient future across Southeast Asia.

ABSTRACT Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for building’s structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.

Financial aid plays a crucial role in ensuring access to education, particularly for individuals from disadvantaged financial backgrounds. Education is a powerful driver of cultural and national development. By facilitating education, financial aid cultivates young professionals capable of driving progress across various fields. Notably, approximately 84 percent of students benefit from financial aid scholarships. In South Africa, bursaries and scholarships play a crucial role in providing essential financial support to students. However, the existing disbursement process is fraught with significant challenges. The manual nature of these processes leads to time-consuming operations, a high risk of errors, delays in fund distribution, and vulnerability to fraud. These inefficiencies hinder the timely delivery of financial aid, posing severe risks to students' academic success. To address these pressing challenges, it is imperative to transition to automated systems that streamline operations, minimise errors and delays, and enhance security against fraud. This transformation will ensure that financial aid reaches deserving students efficiently and securely. Therefore, this study conducts a systematic literature review to thoroughly investigate the challenges faced by financial aid institutions and students in fund disbursement at higher education institutions. Additionally, it examines the potential of Blockchain technology in improving fund disbursement processes. Findings indicate that the current scholarship disbursement procedures in South African institutions are manual, inefficient, and prone to errors, resulting in delays and opportunities for fraud. The urgency of this issue cannot be overstated, as students suffer from both hunger and frustration, while some lose their funds altogether. Importantly, the research highlights that a Blockchain ledger is inherently challenging to tamper with, thereby providing enhanced security. In a Smart Contract Blockchain-based system, every transaction is meticulously recorded on a distributed, publicly visible ledger, ensuring traceability and transparency of all disbursements and significantly reducing the risk of fraud.

Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts (EDSCs) and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in challenge mechanism efficiently detects adversarial behavior. In experiments on image classification and language modeling tasks, ZK-HybridFL achieves faster convergence, higher accuracy, lower perplexity, and reduced latency compared to Blade-FL and ChainFL. It remains robust against substantial fractions of adversarial and idle nodes, supports sub-second on-chain verification with efficient gas usage, and prevents invalid updates and orphanage-style attacks. This makes ZK-HybridFL a scalable and secure solution for decentralized FL across diverse environments.

In the rapidly evolving landscape of wireless communication, the traditional model of data exchange predominantly relies on a centralized infrastructure, where all communications, even between nearby devices, are routed through a base station (BS) and potentially the core network. Device-to-device (D2D) communication emerges as a transformative paradigm that challenges this conventional model. The concept of D2D communication has gained considerable traction, especially with the advent of 4G LTE and its crucial role in 5G and beyond. D2D aims to unlock numerous benefits, including improved spectral efficiency, increased throughput, reduced latency, enhanced energy efficiency, and better network offloading. In this paper, we focus on secure D2D communication using D2D-ECDH key exchange protocol with blockchain smart contracts along with blockchain Verkle tree data structure. This approach helps to improve secure communication between IoT devices and avoids Man-in-the-Middle (MITM) attacks, replay attacks, and central points of failure. The Verkle tree approach provides a smaller proof size to verify the root commitment value compared to the traditional Merkle tree data structure. The simulation has been executed in both Python and Solidity coding. Python has executed in VS Code generator, and Solidity code has executed in Remix IDE, Ethereum network V0.65.0. The Remix IDE was developed in Ganache V2.7.1 for blockchain smart contracts. Compared to the traditional Merkle tree approach, the Verkle tree provides less proof size up to 33 times.

Purpose The construction supply chain (CSC) faces persistent inefficiencies, opacity, and fragmented collaboration. Blockchain technology (BT) has been proposed as a remedy, yet adoption in construction supply chain management (CSCM) remains limited. This study synthesises current knowledge on blockchain-enabled CSCM, focusing on applications, adoption barriers, integration with complementary technologies, and future research directions. Design/methodology/approach A scientometric analysis was employed to examine 145 systematically filtered publications on blockchain-enabled CSCM, providing a critical understanding of research trends, thematic structures, and knowledge developments. A content analysis of 40 selected high-impact articles was conducted to elucidate key applications, challenges, integration pathways, and future research directions. Findings BT’s main applications in CSCM include enhancing transparency and traceability, automating payments through smart contracts, and enabling real-time collaboration when integrated with Building Information Modeling (BIM), Internet of Things (IoT), and digital twins. Adoption, however, is constrained by interoperability challenges, high implementation costs, data privacy concerns, regulatory gaps, and organisational resistance. Research remains fragmented, with limited cross-disciplinary collaboration and a lack of large-scale empirical validations. Originality/value This study synthesises blockchain-CSCM literature using a mixed-method scientometric and content analysis approach, offering a comprehensive thematic synthesis that bridges conceptual propositions with practical adoption challenges. The study advances both academic discourse and practical implementation, guiding the construction industry toward more transparent, efficient, and resilient supply chains.

Blockchain-Based Crop Supply Chain and Traceability Using Smart Contracts

Purpose The classification of cryptocurrencies remains an open challenge to make valid decisions due to their diverse technical structures, financial applications and evolving use cases. The scientific literature does not provide a simple technical categorization that facilitates asset comparison, enhances risk measurement, provides a structured approach to understanding the dependencies between different crypto-assets and facilitates decision-making processes among a wide range of stakeholders. This study proposes a technical categorization framework that classifies cryptocurrencies based on their underlying blockchain infrastructure or smart contract functionalities. Design/methodology/approach The authors designed the categories and classify the top 100 market cap cryptocurrencies with them. To validate the proposal, the same task was executed by using multiple large language models (LLMs), including ChatGPT, Perplexity, Claude and Gemini; with zero-shot classification approach. Findings The results indicate that, when prompted with predefined categories, LLMs achieve substantial agreement with human classification, with ChatGPT demonstrating the best results. Moreover, categorization without any guidance is inconsistent across models, often defaulting to use-case- based groupings. Notably, providing additional information about cryptocurrencies or detailed definitions of categories does not significantly alter classification outcomes, suggesting that LLMs rely predominantly on their internal knowledge base. Research limitations/implications Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Originality/value This study highlights the potential of LLMs as tools for the systematic classification of cryptocurrencies, a key part of an important organizational decision-making process. It is remarked that the importance of having structured categories of cryptocurrencies is for all kinds of decision-makers, including investors, industry stakeholders, fund managers and regulators. Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Highlights

Corruption and transparency: Corruption and absence of transparency have been thorny issues in the banking systems especially in the developing and fragile economies where the inadequacy of the institutions and the absence of supervision erode the public confidence and sustainability of the financial systems. A new digital technology called blockchain has come up and has the potential to solve such problems by radically transforming the way records of financial transactions are stored, checked, and tracked. This paper will discuss how blockchain technology can enhance transparency and curb corruption in the banking system through its centralized properties of decentralization, immutability and distributed consensus. The paper based on the applicable theoretical frameworks, such as Principal–Agency Theory and institutional governance theories, examines why and how blockchain can help curb discretionary authority, increase audit trail, improve regulatory supervision, and curtail fraud and embezzlement, insider abuse opportunities. There is a special focus on the situation in the developing and conflict-prone banking systems, and it is illustrated by the Democratic Republic of the Congo, where money dependency, poor supervision, and low confidence of the population contribute to the risks of corruption. The paper goes further to describe how blockchains are used in practice, including distributed ledgers, smart contracts, and blockchain-enabled KYC and AML systems, and as well as discusses challenges in implementing blockchains, in terms of infrastructure, regulation, and capacity building. On the whole, the research can add to the scholarly and policy discussion about financial governance by showing how blockchain innovation can yield transparency, accountability, and trust restoration in the banking sector and stating the way to introduce it in a responsible and context-aware way.