Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole.
This paper presents a framework for securing blockchain-based IoT systems by integrating Physical Unclonable Functions (PUFs) and Zero-Knowledge Proofs (ZKPs) within a Hyperledger Fabric environment. Our approach leverages PUFs for robust device authentication and ZKPs for privacy-preserving transaction processing, addressing key challenges of security, privacy, and scalability in IoT systems. The framework’s architecture utilizes Hyperledger Fabric’s modular design and private channels to enhance scalability. Off-chain experimental results demonstrate the framework’s feasibility, with compact proof sizes (median 805 bytes) and efficient processing times (average 2,800 ms end-to-end). A comprehensive security analysis shows the framework’s resilience against various attacks, including device impersonation and data tampering. This work provides a foundation for secure and scalable blockchain-based IoT systems, with directions for future on-chain implementation and optimization for resource-constrained devices.
Open access
3 source records
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Advanced Steganography and Watermarking Techniques
The rapid development of Bitcoin and blockchain technology is shocking. In the development of Bitcoin and other industries, machine learning has contributed a lot and has unlimited potential. It can not only analyze the data in the transaction process, but also bring security and predict the development trend of the market. The combination of multiple technologies promotes the efficiency of Bitcoin transactions, and provides effective support for making correct decisions, which is enough to show that financial technology can still undergo unpredictable changes in the next stage of development. In this research, the application of machine learning technology in the development of Bitcoin is analyzed in depth, especially in improving efficiency, improving intelligent contracts, monitoring transactions and so on. Through the analysis, efficiency and prediction accuracy of the model will change positively because of the application of algorithm and data processing technology. This study also points out the significance of protecting user privacy and enhancing data security, which brings effective strategies for the development of Bitcoin technology, the wide use of encryption technology and the improvement of regulatory efficiency, and fully taps the potential of machine learning.
I Gede Agus Krisna Warmayana, Yuichiro Yamashita, Nobuto Oka "Decentralized Materials Data Management using Blockchain, Non-Fungible Tokens, and Interplanetary File System in Web3" Journal of Applied Data Sciences, 2025, Vol.6, No.1, p.742-752 https://doi.org/10.47738/jads.v6i1.380 掲載
The safety and security of e-commerce transactions are critical in today's digital landscape, where cyber risks abound. Blockchain technology's decentralized, transparent, and immutable ledger system offers a viable solution to these concerns. This study investigates the use of Distributed Ledger Technology (DLT), specifically Decentralized Identifiable Distributed Ledger Technology (DIDLT), in conjunction with the innovative Blockchain Consent Algorithm (BCA), to improve the safety and security of e-commerce transactions. Secure data storage and retrieval in e-commerce can be achieved by accurate digital signature production, key generation, blockchain building, and validation. This paper investigates how DIDLT, which combines decentralized identity management with blockchain technology, and BCA, a cutting-edge consensus algorithm designed for blockchain environments, work together to protect e-commerce transactions from identity theft, payment fraud, and data manipulation. This paper throws insight into DIDLT and BCA's potential to transform the e-commerce safety and security market by providing an in-depth analysis of their implementation. Using the incorporated DIDLT-BCA model significantly improves the safety effectiveness of the network, resulting in 98% security, shorter performance times of as much as 150 milliseconds, and mining times of up to 0.98 s.
Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital crypto currencies, such as Bit coin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.
This study aims to delve into the knowledge graph, functional pathways, and qualitative logic among elements such as network trust, blockchain organization, user identity, big data, and information security in the digital economy era. Through an analysis of the endogenous and exogenous explicit management logic relationships among these elements, this study innovatively constructs three primary dimensions and nine secondary dimensions of digital identity attributes for users. Additionally, it establishes a collaborative management mechanism between government organizations and non-governmental blockchain alliances corresponding to identity attributes based on the delegated proxy mechanism. Furthermore, it reconstructs the big data chain management framework for user digital identity under the zero-trust model and establishes a management process for blockchain digital identity information security protection under the zero-trust model. These efforts provide innovative solutions with both research and application value for the “virtual-real integration” global security governance of the metaverse, digital economy, and digital government.
Data provenance is critical for establishing the origin, authenticity, and integrity of data in distributed environments. Traditional provenance systems often face challenges such as tampering, lack of transparency, and centralized trust issues. Blockchain technology, with its immutable ledger and decentralized consensus mechanisms, provides a promising solution to these challenges. This article explores blockchain-based cryptographic methods that enhance the security and reliability of data provenance systems. We discuss key cryptographic primitives such as digital signatures, hash chains, and zero-knowledge proofs integrated within blockchain frameworks to guarantee secure and verifiable provenance records. The study also highlights practical implementations and identifies open challenges for future research
This paper investigates the integration of blockchain technology into core systems within institutions of higher education, utilizing the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework as a guiding framework. It supplies definitions of key terminology including blockchain, consensus mechanisms, decentralized identity, and smart contracts, and examines the application of secure blockchain across various educational functions such as enrollment management, degree auditing, and award processing. Each facet of the NIST Framework is utilized to explore the integration of blockchain technology and address persistent security concerns. The paper contributes to the literature by defining blockchain technology applications and opportunities within the education sector.
Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.
Leonardo T. Kimura, Felipe K. Shiraishi, Ewerton R. Andrade, Tereza Cristina Melo de Brito Carvalho · 5 authors
The bioeconomy, an industrial production model based on biological resources and sustainable development, can be considered an emerging opportunity for biodiversity-abundant regions, such as the Amazon rainforest. However, existing genomic repositories lack data traceability and economic benefit-sharing mechanisms, resulting in limited motivation for data providers to contribute. To address this challenge, we present an implementation of Amazon Biobank, a community-driven genetic database. By leveraging blockchain and peer-to-peer (P2P) technologies, we enable distributed and transparent data sharing; meanwhile, by using smart contracts directly registered in the system, we enforce fair benefit-sharing among all system participants. Moreover, Amazon Biobank is designed to be auditable by any user, reducing the need for trusted system managers. This paper aims to validate this model, by describing the implementation of a prototype using Hyperledger Fabric and BitTorrent and evaluating its performance. Our results show that the prototype can support at least 400 transactions per second in a small network, a number that can be further improved by adding new nodes or allocating additional computational resources. We conclude that the Amazon Biobank proposal is technically feasible, and its real-world deployment has the potential to foster sustainable development in high-biodiversity regions, in addition to promoting collaborative biotechnology research.
Permissionless blockchain operates as a fully decentralized, transparent, and immutable ledger. Preserving privacy in such systems is a complex challenge, as privacy cannot rely on restricting access or deleting data. Bitcoin, the first application of blockchain technology, was initially praised as an anonymous digital currency, but the transactions on the network have been shown to be relatively easy to trace. This realization has led to the development of advanced privacy-enhancing mechanisms with stronger anonymity guarantees. This thesis offers a comprehensive overview privacy-preserving techniques for permissionless blockchain through a systematic tertiary review of existing surveys. It identifies and categorizes key techniques such as zero-knowledge proofs, ring signatures, homomorphic encryption, secure multi-party computation and decentralized mixing protocols. Their capabilities to mitigate risks of linkability and information leakage, as well as limitations like computational overhead, are examined. Furthermore, unresolved challenges and research interests in the field are analyzed. By consolidating fragmented insights into a coherent and accessible resource, this work aims to support the privacy-aware development and adoption of blockchain applications. The findings highlight a fundamental trade-off between the privacy capabilities, efficiency, and trust assumptions of existing techniques. Privacy in permissionless blockchain often requires computationally complex cryptographic methods, leading to significant delays and increased costs for users. Efficiency can be improved by assuming some level of trust in entities or hardware, but this may conflict with the principle of decentralization. Although many powerful techniques exist, there is no universal solution, and the best results are achieved with combining techniques on a case-by-case basis. Current research on permissionless blockchain privacy focuses on improving efficiency, interoperability and usability of privacy preserving techniques. Additionally, regulatory compliance and accountability are critical concerns, as the technology must comply with privacy regulations while preventing anonymity in illegal activities such as money laundering.
Thanks to developments in artificial intelligence (AI), cloud computing, cyber security, and other game-changing technologies, the information technology (IT) landscape is changing quickly. With an emphasis on artificial intelligence (AI), multi-cloud strategies, cybersecurity resilience, quantum computing, the Internet of Things (IoT), DevOps, blockchain, and Environmental, Social, and Governance (ESG) activities, this article offers a thorough examination of current IT developments influencing companies in 2024. Business operations are being completely transformed by artificial intelligence (AI) and machine learning (ML), which improve automation and personalization while bringing up moral questions of justice and transparency. Organizations are using AI-driven threat detection and zero-trust architecture more frequently as cyber threats increase in order to strengthen cybersecurity resilience. As cloud computing advances toward multi-cloud and hybrid models, businesses can benefit from increased scalability, flexibility, and reduced vendor lock-in. Though they are still in the experimental stage, emerging technologies like quantum computing offer promising improvements in computational capacity and the ability to solve complicated problems. Furthermore, the integration of 5G with IoT devices is improving real-time data processing in a number of industries, including logistics and healthcare. Initially restricted to cryptocurrencies, blockchain technology is increasingly being used in secure data management and decentralized finance (DeFi) applications. Lastly, companies are embracing green IT solutions and sustainable practices as part of the growing popularity of ESG activities. This paper clarifies these important IT trends through a detailed literature study, industry research, and expert insights, offering a thorough picture of how companies should strategically navigate the continuing digital revolution.
Usman Khalil, Mueen Uddin, Owais Ahmed Malik, Wee-Hong Ong
The blueprint of the proposed Decentralized Smart City of Things (DSCoT) has been presented with smart contracts development and deployment for robust security of resources in the context of cyber-physical systems (CPS) for smart cities. Since non-fungibility provided by the ERC721 standard for the cyber-physical systems (CPSs) components such as the admin, user, and IoT-enabled smart device/s in literature is explicitly missing, the proposed DSCoT devised the functionality of identification and authentication of the assets. The proposed identification and authentication mechanism in cyber-physical systems (CPSs) employs smart contracts to generate an authentication access code based on extended non-fungible tokens (NFTs), which are used to authorize access to the corresponding assets. The evaluation and development of the extended NFT protocol for cyber-physical systems have been presented with the public and private blockchain deployments for evaluation comparison. The comparison demonstrated up to 96.69% promising results in terms of execution cost, efficiency, and time complexity compared to other proposed NFT-based solutions.
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.
The sixth generation (6G) wireless cellular networks are anticipated to include the most recent advancements in network infrastructure and new technological discoveries.In addition to exploring more spectrum at high-frequency bands, it will bring together cutting-edge technical trends like blockchain, artificial intelligence (AI), and connected robotics.6G and Next-Generation Internet: Under Blockchain Web3 Economy by Abdeljalil Beniiche explores the human-centeredness of blockchain and Web3 economy for the 6G era.
Rafael Belchior, Dimo Dimov, Zahary Karadjov, Jonas Pfannschmidt · 6 authors
The field of blockchain interoperability plays a pivotal role in blockchain adoption. Despite these advances, a notorious problem persists: the high number and success rate of attacks on blockchain bridges. We propose Harmonia, a framework for building robust, secure, efficient, and decentralized cross-chain applications. A main component of Harmonia is DendrETH, a decentralized and efficient zero-knowledge proof-based light client. DendrETH mitigates security problems by lowering the attack surface by relying on the properties of zero-knowledge proofs. The DendrETH instance of this paper is an improvement of Ethereum’s light client sync protocol that fixes critical security flaws. This light client protocol is implemented as a smart contract, allowing blockchains to read the state of the source blockchain in a trust-minimized way. Harmonia and DendrETH support several cross-chain use cases, such as secure cross-blockchain bridges (asset transfers) and smart contract migrations (data transfers), without a trusted operator. We implemented Harmonia in 9K lines of code. Our implementation is compatible with the Ethereum Virtual Machine (EVM) based chains and some non-EVM chains. Our experimental evaluation shows that Harmonia can generate light client updates with reasonable latency, costs (a dozen to a few thousand US dollars per year), and minimal storage requirements (around 4.5 MB per year). We also carried out experiments to evaluate the security of DendrETH. We provide an open-source implementation and reproducible environment for researchers and practitioners to replicate our results.
Md. Raisul Hasan Shahrukh, Md. Tabassinur Rahman, Nafees Mansoor
Blockchain, a decentralized technology that provides unrivaled security, transparency, and process validation, is redefining the operational landscape across numerous industries. This article focuses on the development of an innovative consortium blockchain-based financial distribution application. This paper illuminates the transformative role of blockchain technology in a variety of sectors by drawing on a plethora of academic literature and current industry practices. It demonstrates the diverse applications of blockchain, ranging from remittances to lending and investments in finance to data administration in healthcare and supply chain tracking. The paper reveals the design and potential of a consortium blockchainbased application for financial distribution. Utilizing the capabilities of Hyperledger Besu, the application is tailored to improve security, scalability, and interoperability, thereby contributing to a more integrated financial ecosystem. The investigation sheds light on the combination of consortium blockchain’ controlled access and Hyprledger Besu’ comprehensive functionality, proposing a secure, transparent, and efficient financial transaction environment. The investigation serves as a resource for academics, industry professionals, and policymakers alike, highlighting the vast potential of blockchain technology, enabled by platforms such as Hyperledger Besu, in accelerating the evolution of traditional systems toward a more decentralized, secure, and efficient future.
A adoção de blockchain e Contratos Inteligentes (CI) tem se demonstrado uma opção valiosa para organizações inovarem em seus sistemas de informação. Nesse sentido, presencia-se uma adesão ao desenvolvimento open source por parte dos envolvidos em tal ecossistema, especialmente com base na plataforma Ethereum e no uso do GitHub. Logo, nota-se uma formação de um contexto sociotécnico para desenvolvimento de CI através do uso de plataformas para hospedagem de código-fonte como, por exemplo o GitHub. Porém, apesar da presença de pesquisas prévias sobre manutenção de CIs, constata-se uma lacuna de pesquisa como foco na compreensão de componentes sociotécnicos em tal domínio. Nesse sentido, sob um escopo metodológico exploratório pautado em Mineração de Repositórios de Software na Teoria Sociotécnica, este traba- lho tem como objetivo principal analisar componentes sociotécnicos em repositórios open source de contratos inteligentes implantados na plataforma Ethereum. Como contribuições alcançadas, quatro perspectivas principais foram contempladas: 1) na discussão sobre efeito do deploy na Ethereum sobre os aspectos colaborativos (com foco em contribuidores, commits e issues); 2) na compreensão sobre a caracterização dos contribuidores envolvidos nos projetos; 3) na categorização sobre o que mudou quanto à evolução de software em tais projetos a partir das mensagens de commits e 4) no entendimento sobre a relação entre métricas de mercado (valor de mercado e volume e evolução de software (em termos de commits).
Qianrui Zhao, Yinan Wang, Bo Yang, Ke Shang · 8 authors
Cross-chain bridges are crucial mechanisms for facilitating interoperation between different blockchains, allowing the flow of assets and information across various chains. Their pivotal role and the vast value of assets they handle make them highly attractive to attackers. Major security incidents involving cross-chain bridge projects have been occurring frequently, resulting in losses of several billion due to cyber attacks. The diversity of vulnerability exploitation methods by hackers is vast, but not entirely untraceable. There are scarce research outcomes studying cross-chain bridge cyber incidents, and we have conducted a study based on the most recent cross-chain bridge security incidents. We introduce the working principles, components, and architecture of cross-chain bridges, explain the categorization mechanisms of the trust layer in cross-chain bridges, summarize four categories of hacker vulnerability exploitation techniques from real cases, and propose preventative measures for cross-chain bridge security.
A Bitcoin Generator Scam (BGS) is a type of cyberattack in which scammers promise to provide individuals with free cryptocurrencies if they pay a mining fee. Although graph neural networks (GNNs) have been used for detecting other cryptocurrency frauds, the usefulness of these methods for BGS detection has not been studied. In this paper, we carry out extensive experiments to assess the use of both standard machine learning (ML) methods and GNNs to detect Bitcoin transactions associated with activities stemming from Bitcoin Generator Scams. We observe that the over-smoothing problem exists in GNNs designed for BGS detection and show that Random Walk Positional Encoding (RWPE) allows representing long-range interactions between far-away transactions in GNNs without causing over-smoothing. We show that the General, Powerful, Scalable (GPS) Graph Transformer with RWPE outperforms both GNN and ML based state-of-the-art fraud detection methods in Bitcoin Generator Scams. We also analyze the effectiveness of Breadth First Search (BFS) for graph sampling and show that it should not be used as it induces bias toward the subnetwork structure. We propose the Random First Search (RFS) sampling alternative and show that this is a more suitable solution.
In order to solve the problems of imperfect transaction legitimacy verification strategies for protecting account balances and transaction amounts in privacy protection of consortium blockchain, and the low efficiency of the basic encryption algorithm Paillier, a <sup>+</sup>HomElG zero-knowledge proof protocol for consortium blockchain transfer privacy protection was proposed. A consortium blockchain transfer privacy protection application was constructed based on PBFT, which expounded the consensus interaction scenario of zero-knowledge proof of homomorphic encryption. The transaction amount and balance of account were encrypted by the <sup>+</sup>HomElG algorithm, and the zero-knowledge proof of the ciphertext was designed with the Σ protocol. The non-interactive zero-knowledge was designed through the idea of the Fiat-Shamir algorithm processes such as the proof of equality, the amount of the transaction greater than zero and the balance of the transfer party not less than zero in the proof of range. The protocol was proved to be correct, complete and zero-knowledge under the DDH. A consortium blockchain transfer privacy protection prototype system based on Hyperledger Fabric was constructed. The results verified that the protocol can realize ciphertext transactions to protect balance of account and transaction amount under the condition of non-interactive zero-knowledge proof. When the key length is 3072 bit and the data length is a 12-bit decimal integer, the efficiency of the <sup>+</sup>HomElG algorithm is 150.3 ms, and the efficiency of the proof of equality, the amount of the transaction greater than zero and the balance of the transfer party not less than zero in the proof of range are 482.3 ms, 209.3 ms and 261.3 ms respectively. Compared with the existing protocols, the proposed <sup>+</sup>HomElG algorithm is more efficient, and its transaction legitimacy verification strategies such as equality proof and range proof are more perfect and efficient. The proposed protocol can meet the privacy protection requirements of consortium blockchain transfer transactions.
To solve the problem of unreasonable distribution of PoS block rewards, a proof of stake based on incentive (Incentive-PoS)consensus algorithm was proposed. Firstly, the research problem was described. A PoS determined that nodes with more coins have a greater chance of obtaining accounting rights, and the block reward was exclusively owned by the block producer. Secondly, in order to solve the problem of reward distribution, a PoS consensus algorithm based on incentive mechanism was proposed, Shapley′s principle in game theory was uesd to redistribute block rewards. Nodes with high credibility and active participation in consensus would receive dividends, and made small nodes more likely to obtain benefits. Finally, the simulation experiment and result analysis of the improved algorithm were carried out. Compared with the original algorithm, the improved scheme had a more reasonable performance in the distribution of income, and increased the number of nodes receiving dividends, reduced the gap between the rich and the poor, and improved the enthusiasm of consensus. And the throughput, latency, and security were significantly improved. It was beneficial to improve the stratification phenomenon caused by the excessive wealth gap in the blockchain, and could further promote the healthy operation and development of the blockchain network.
BRC-20 (short for Bitcoin Request for Comment 20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional ERC-20 token standards on Ethereum, BRC-20 introduces non-fungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this paper, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multi-dimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin's functionality and applicability, it may still not match Ethereum's abundance of decentralized applications and similar ecosystems.