Blockchain Papers

Ye Bai, Debiao He, Zhichao Yang, Min Luo · 5 authors

No abstract is available for this record.

Uzay Işın Alıcı, Adem Orsdemir, Muhammad Tahir, Alptekın Küpçü

Blockchain technology allows us to make trust-based transactions without third-party intermediaries. However, its rapidly developing nature brings serious security vulnerabilities. These vulnerabilities are a research priority because smart contracts (SC) maintained on the blockchain system cannot be modified or reversed after deployment. Our research indicates that Deep Learning (DL) and Machine Learning (ML) methodologies have recently become popular for detecting these vulnerabilities in SC. This systematic literature evaluation highlights its contributions compared to similar studies with the most common vulnerabilities.

Jhansy Archana Vasigani, M. Vivekanandan, Subhankar Ghatak

No abstract is available for this record.

Ayushman Sharma, Praveen Bohara, Manish Tiwari, Maad M. Mıjwıl · 6 authors

No abstract is available for this record.

Ji-Won Kang, Daihyun Kwon, Sun‐Yong Choi

This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (Wavelet–CNN, Wavelet–Transformer, and CNN–Transformer) confirm that the proposed Wavelet–CNN–Transformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the Wavelet–Transformer highlights CNN’s critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models.

Lisa Mmesoma Udechukwu, Oluwadayo Mafolasere Olaniyi, Eweoya Adebukola Oluyinka, Omobolaji Olufunmilayo Olateju · 5 authors

This research developed a blockchain-enabled framework to enhance secure credentialing and access management for remote healthcare providers and patients across fragmented digital health platforms. Addressing inefficiencies in traditional systems such as lengthy verification delays and data silos, the study employed a design science approach, integrating Hyperledger Fabric and Ethereum smart contracts. Simulations using synthetic healthcare datasets demonstrated a significant improvement, including a 99.99% reduction in credential verification time (to 14 seconds), a 650% throughput increase (to 1,876 TPS), and a 94.7% reduction in security breaches, with 97.8% interoperability success across 234 systems. The framework achieved 99.93% authentication accuracy and 41% administrative cost savings. While results show strong potential, the reliance on simulations may not capture full real-world complexities, and high initial deployment costs remain a constraint. Regulatory compliance, particularly with evolving standards such as HIPAA, was considered essential for implementation. Future work will focus on real-world pilot deployments, AI-driven fraud detection, and the establishment of standardized protocols to support scalability and interoperability. Overall, this study advances secure and efficient healthcare delivery by enabling real-time credentialing and interoperable access, fostering patient-centric care in telemedicine.

Mario Trerotola, Davide Calvaresi

Distributed Ledger Technologies (DLTs) fuse cryptographic immutability with decentralized consensus, transforming global finance, simultaneously hindering the forensic reconstruction of illicit value flows. This paper presents a systematic mapping of financial transaction tracing on DLTs through a rigorously designed Systematic Literature Review (SLR). Six research questions - covering enabling technologies, privacy primitives, tracking methods, structural limits, proposed mitigations, and future directions - guided the search. Three reviewers screened 120 publications from 2017–2025, resolving disagreements by adjudication and distilling 21 primary studies (17.5 % acceptance). The corpus converges on four technical pillars: heuristic/graph-based address clustering, machine-learning anomaly detection, cross-ledger correlation frameworks, and privacy-enhancing constructs such as ring signatures and zk-SNARKs. Our synthesis exposes a persistent tri-lemma among scalability, attribution accuracy, and privacy compliance, exacerbated by heterogeneous protocol designs and data-retention costs. To reconcile these issues, we articulate a four-layer tracing architecture that integrates high-throughput multi-chain ingestion, cache-efficient temporal graph indexing, explainable risk scoring, and privacy-preserving off-chain fusion with KYC anchors. This blueprint offers regulators, investigators, and researchers a scalable, GDPR-aligned pathway for illuminating opaque financial networks, while establishing a consolidated knowledge base and a forward research agenda for next-generation DLT traceability.

Luis de‐Marcos, Adrián Domínguez‐Díaz, Javier Junquera-Sánchez, Carlos Cilleruelo · 5 authors

The Dark Web, a hidden segment of the internet, has become a hub for illicit activities, facilitated by various forms of digital identification (IDs) such as email addresses, Telegram accounts, and cryptocurrency wallets. This study conducts a comprehensive analysis of the Dark Web’s identification and communication patterns, focusing on the roles of different ID types and their associated activities. Using a dataset of Dark Web documents, we construct and analyze a bipartite network to model the relationships between IDs and web documents, employing graph–theoretical metrics such as degree centrality, closeness centrality, betweenness centrality, and k-core decomposition, while analyzing subnetworks formed by ID type. Our findings reveal that Telegram forms the backbone of the network, serving as the primary communication tool for hacking-related activities, particularly within Russian-speaking communities. In contrast, email plays a more decentralized role, facilitating finance–crypto and other activities but with a high level of fragmentation and English as the predominant language. XMR (Monero) wallets emerge as a key component in financial transactions, forming a cohesive subnetwork focused on cryptocurrency-related activities. The analysis also highlights the modular and hierarchical nature of the Dark Web, with distinct clusters for hacking, finance–crypto, and drugs–narcotics, often operating independently but with some cross-topic interactions. This study provides a foundation for understanding the Dark Web’s structure and dynamics, offering insights that can inform strategies for monitoring and mitigating its risks.

Munish Bhatia, K.M. Charul

No abstract is available for this record.

S Ezhilmathi, S. Selvakumara Samy

No abstract is available for this record.

Kuan Fan, Jie Bai, Shiyue Zhou, Wenbo Shi · 5 authors

No abstract is available for this record.

Prince Kumar

As healthcare ecosystems shift toward digital-first operations, personal health data faces unprecedented security and privacy risks from increasingly sophisticated cyber threats. This paper examines how the integration of Artificial Intelligence (AI), including Agentic AI, blockchain, and cloud computing, can establish an advanced security framework for resilient healthcare data management. Unlike traditional siloed systems, the proposed model leverages AI-driven anomaly detection, multi-agent orchestration, and explainable AI (XAI) for real-time threat prediction and adaptive defense. Blockchain contributes decentralized trust, tamper-proof auditability, and consent-enforcing smart contracts, while cloud platforms deliver elastic scalability, encrypted storage, and hybrid multi-cloud deployment models. The framework also incorporates federated learning, Model-Chaining Protocols (MCPs), and Zero-Knowledge Proofs (ZKPs) to enhance interoperability, preserve privacy, and enable verifiable compliance. Findings highlight significant improvements in confidentiality, integrity, and availability (CIA) of healthcare data, while simultaneously addressing regulatory obligations such as HIPAA and GDPR through embedded governance and risk orchestration layers. Despite challenges around system complexity and policy harmonization, the paper provides a state-of-the-art synthesis and proposes actionable best practices for healthcare practitioners and policymakers, including adopting continuous AI-powered risk monitoring, blockchain-based patient-centric data ownership, and automated compliance verification mechanisms. Overall, the convergence of AI, blockchain, and cloud technologies—augmented by governance-driven orchestration—offers a future-proof, cyber-resilient architecture for safeguarding personal health data in digital-first healthcare ecosystems.

Muhammad Tahir, Adem Orsdemir, Fiza Siyal, Uzay Işın Alıcı · 6 authors

The rapid growth in blockchain technology adoption has highlighted the significance of security in Ethereum smart contracts. Due to its immutable nature, post-deployment rectification is not possible, and vulnerabilities such as reentrancy have led to substantial financial losses in recent years, making it a pressing research priority for its timely detection. Along with static and dynamic analysis tools, recent studies have shown promising results using Deep Learning (DL) and Machine Learning (ML) techniques for vulnerability detection using imagebased methods. Although these methods often suffer from high false positive rates and limited interpretability. To address these issues, we proposed an interpretable One-dimensional Convolutional Neural Network (1D CNN), a lightweight DL framework with integrated Gradients, an attribution for the Explainable AI (XAI) framework. This framework processes smart contract opcode in a series of sequences rendered as RGB-encoded strips, enabling effective feature extraction while preserving the contract semantics and execution order. Trained on a publicly available labeled comprehensive dataset named Messi-Q, which has already been used in prominent studies in the field. Approach achieves over 97% classification accuracy in detecting reentrancy vulnerability. More importantly, it provides fine-grained, opcode-level attributions offering a scalable and interpretable path forward for smart contract analysis.

Rini Hardiyanti, Amil Ahmad Ilham, Ady Wahyudi Paundu

This research proposes the use of AES-256-CBC encryption, decentralized storage using Interplanetary File System (IPFS), and CID hash logging into the Ethereum. The system was tested using files of 5, 10, 15 and 100 MB, and shows that the encryption-decryption process has no significant impact on system performance. The security evaluation resulted 6 out of 7 test scenarios successfully prevented data theft, manipulation, and duplication after using attack simulations such as reply attacks, bit-flipping, Man-in-the-Middle (MITM), and Brute Force attacks. The total cost for logging CID hashes to Ethereum was $0.00913-0.01080$ ETH. Although throughput performance and execution time are volatile in both proposed and comparison system, the proposed system is superior in terms of system security and cost efficiency, making it feasible to use for e-learning content protection.

Shoufeng Cao

The study addresses the intersection of indigenous food sovereignty and data sovereignty in the digital era by exploring community-governed digital infrastructures for indigenous bushfood systems. It explores the use of blockchain networks as a digital commons to safeguard transparent, tamper-proof records and ethical access to indigenosu data or knowledge. Through a participatory design approach embedded in cultural protocols and practices within the Australian bushfood sector, non-fungible tokens (NFTs) were designed to uphold indigenous sovereignty and collective benefit from the research, commerclisation and trade of bushfood species and derived products. This study presented a blockchain-enabled NFT infrastructure incorporating traditional owner tokens (TOTokens) for representing resource and cultural custodianship and enabling usage tracking, and authentic provenance tokens (APTokens) for tracing bushfood provenance and associated traditional ownership. This dual NFT infrastructure design enables the unique digital representation of bushfood and associated traditional ownership, while also provides a socio-economic mechanism to monetise traditional ownership across bushfood research and commerce scenarios. This dual NFT infrastructure is underpinned by smart contracts that enable the tradability and/or transferability of TOTokens and APTokens to automate governance rules, ethical access and collective benefit sharing, without reliance on external authorities. A proof-of-concept was piloted and tested on Polygon a public blockchain demonstrating its technical feasibility. The blockchain-based NFT infrastructure aligns with indigenous data sovereignty principles, CARE and FAIR data frameworks, and can integrate with Internet of things (IoTs), AI, machine learning and data analytics to conduct culturally grounded and ethics-controlled deep eResearch for business innovation and industry practice.

Joy Nneamaka, Emmanuel Ojo, Chika Oliver Ujah

This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability. • Decentralized renewable energy (DRE) is paving the way for fair energy access across Sub-Saharan Africa. • ii. DRE systems are all about empowering communities, promoting energy democracy, and building resilience against climate change. • iii. Unfortunately, there are policy, financial, and technical hurdles that hold back the widespread adoption of DRE in the area. • iv. Various case studies showcase a range of DRE strategies and creative financing solutions. • v. For a successful shift to sustainable energy, integrated policy reforms and regional collaboration are essential.

Jinni Yang, Shuang Liu, Surong Dai, Yaozheng Fang · 6 authors

Smart contract vulnerability detection has attracted increasing attention due to billions of economic losses caused by vulnerabilities. Existing smart contract vulnerability detection methods have high false negative and high false positive rates. To address these issues, we present ByteEye, a bytecode level smart contract vulnerability detection framework with Graph Neural Networks (GNNs). ByteEye first constructs an edge-enhanced Control Flow Graph (CFG) to maintain rich information from the low-level bytecode with low latency. ByteEye also designs and incorporates both general information and vulnerability-specific information into its detection method as bytecode level features. Furthermore, ByteEye flexibly supports machine/deep learning models, especially with graph neural networks, which can facilitate vulnerability detection precisely. The extensive experimental results highlight that ByteEye outperforms the state-of-the-art approaches on all three types of vulnerability detection. ByteEye can achieve an average of 35.29%, 43.95%, and 6.38% higher on F1 than the bytecode level best-performed baseline on reentrancy vulnerability, timestamp dependency vulnerability, and integer overflow/underflow vulnerability, respectively. Moreover, ByteEye can detect 361 new vulnerabilities in real-world smart contracts, which are reported for the first time. ByteEye enhances control flow information, designs general bytecode-level features with expert knowledge, and flexibly supports deep learning models, particularly GNNs, thus achieving high detection effectiveness.

Ravi Gupta, Guneet Bhatia

Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demonstrates significant performance achievements. InkubaLM on Jetson Nano achieved a Time-To-First-Token (TTFT) of 129 ms, an average inter-token latency of 33 ms, and a throughput of 45.2 tokens per second while consuming 8.4 W. On Raspberry Pi 4B, InkubaLM also led with 326 ms TTFT and 15.9 tokens per second at 5.8 W power consumption. The framework consistently delivered high multilingual quality, averaging a BLEU score of 0.688, cultural relevance of 4.4/5, and fluency of 4.2/5 across tested African languages. Through potential partnerships with active community organizations including African Youth & Community Organization (AYCO) and Florida Africa Foundation, this research aims to establish a practical foundation for accessible, localized, and sustainable AI-driven education in resource-constrained environments. Keeping focus on long-term viability and cultural appropriateness, it contributes to United Nations SDGs 4, 9, and 10. Index Terms - Multi-Agent Systems, Edge AI Computing, Educational Technology, African Languages, Rural Education, Sustainable Development, UN SDG.

Chong Chen, Jiachi Chen, Lingfeng Bao, David F. Lo · 10 authors

Smart contract vulnerabilities, particularly improper Access Control that allows unauthorized execution of restricted functions, have caused billions of dollars in losses. GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files-these serve as intermediate development artifacts that must be compiled and packaged before deployment. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can introduce significant security risks. Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories, as they typically require the target contract to be compilable to generate an abstract representation for further analysis. This paper presents TRACE, a tool designed to secure non-compilable smart contract repositories against access control vulnerabilities. TRACE employs LLMs to locate sensitive functions involving critical operations (e.g., transfer) within the contract and subsequently completes function snippets into a fully compilable contract. TRACE constructs a function call graph from the abstract syntax tree (AST) of the completed contract. It uses the control flow graph (CFG) of each function as node information. The nodes of the sensitive functions are then analyzed to detect Access Control vulnerabilities. Experimental results demonstrate that TRACE outperforms state-of-the-art tools on an open-sourced CVE dataset, detecting 14 out of 15 CVEs. In addition, it achieves 89.2% precision on 5,000 recent on-chain contracts, far exceeding the best existing tool at 76.9%. On 83 real-world repositories, TRACE achieves 87.0% precision, significantly surpassing DeepSeek-R1's 14.3%.

To-Wen Liu, Matthew Green

Digital transactions currently exceed trillions of dollars annually, yet traditional paper-based agreements remain a bottleneck for automation, enforceability, and dispute resolution. Natural language contracts introduce ambiguity, require manual processing, and lack computational verifiability, all of which hinder efficient digital commerce. Computable legal contracts, expressed in machine-readable formats, offer a potential solution by enabling automated execution and verification. Blockchain-based smart contracts further strengthen enforceability and accelerate dispute resolution; however, current implementations risk exposing sensitive agreement terms on public ledgers, raising serious privacy and competitive intelligence concerns that limit enterprise adoption. We introduce zk-agreements, a protocol designed to transition from paper-based trust to cryptographic trust while preserving confidentiality. Our design combines zero-knowledge proofs to protect private agreement terms, secure two-party computation to enable private compliance evaluation, and smart contracts to guarantee automated enforcement. Together, these components achieve both privacy preservation and computational enforceability, resolving the fundamental tension between transparency and confidentiality in blockchain-based agreements.

Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh

Smart Contracts (SCs), self-executing programs on blockchain platforms, are transforming industries such as banking, healthcare, and supply chains through automated, trustless transactions. However, their inherent vulnerabilities have led to severe financial and operational losses, with large-scale exploits causing substantial economic damage. Machine Learning (ML) has emerged as a promising approach for SC vulnerability detection, yet its effectiveness, adaptability, and generalizability remain insufficiently explored. This article comprehensively classifies current Ethereum SC vulnerabilities and attacks. It also surveys 108 ML-based detection methods, covering both traditional models and a structured taxonomy of advanced approaches such as GNN-based, LLM-based, contrastive learning, ensemble, hybrid, meta-learning, and transfer learning techniques. The strengths, limitations, and practical challenges of these methods are systematically analyzed, with particular attention to factors such as detection stages, classification problems, dataset characteristics, feature engineering, performance evaluation, generalizability, detection capability, model aging, and ethical and privacy implications. Additionally, existing datasets on SC vulnerabilities are reviewed and consolidated. By integrating these insights, this work provides actionable guidelines and a foundation for building secure, resilient, and trustworthy SC ecosystems.

Christopher De Baets, Basem Suleiman, Armin Chitizadeh, Imran Razzak

Abstract In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including K-nearest neighbours, random forest, decision trees, extreme gradient boosting, and support vector machines, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests future directions. The insights gathered are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security.

Sultan Kocaman, Ali Dogan

Decentralized Autonomous Organization operates without a central entity, being owned and governed collectively by its members. In this organization, decisions are carried out automatically through smart contracts for routine tasks, while members vote for unforeseen issues. Scalability in decisionmaking through voting on proposals is essential to accommodate a growing number of members without sacrificing security. This paper addresses this challenge by introducing a scalable and secure DAO voting system that ensures security through Groth16 zk-SNARKs and exponential ElGamal encryption algorithm while achieving scalability by verifiably delegating heavy computations to untrusted entities. While offline computation on the exponential ElGamal homomorphic encryption algorithm is enabled to reduce the computational cost of the blockchain, Groth16 is allowed to maintain robust off-chain calculation without revealing any further details. Specifically, the Groth16 proof guarantees that (i) the encrypted votes accurately reflect the voter's voting power, ensuring no unauthorized weight manipulation; (ii) only valid non-negative vote values are encrypted, preventing unintended or malicious vote tampering; and (iii) the homomorphic summation is performed correctly. The implementation shows that the proofs are verified remarkably fast, making the S2DV protocol highly suitable for scalable DAO voting, while preserving the security of the election.

Kadhim Abdulfadhil Gatea

This paper addresses the challenge of designing secure and private digital credentialing systems by leveraging advanced mathematical primitives from applied cryptography. The core of our proposed solution is the application of Zero-Knowledge Proofs (ZKPs), a class of cryptographic protocols that allows for the verification of assertions without disclosing the underlying secret data. We introduce a formal, layered architecture that demonstrates how the mathematical properties of ZKPs can be systematically translated into a robust, large-scale information system. The framework's design is validated against the complex requirements of the academic domain, which serves as a rigorous testbed for our architectural approach. The primary contribution is a blueprint for integrating complex cryptographic protocols into practical system design, demonstrating how mathematical guarantees of privacy can be preserved in a distributed and verifiable manner. This work provides a novel contribution at the intersection of applied cryptography, system architecture, and information security.