Blockchain-Research and Applications最新文献_第3页

Robust cooperative spectrum sensing in cognitive radio blockchain network using SHA-3 algorithm 基于SHA-3算法的认知无线电区块链网络鲁棒协同频谱感知

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100224

Evelyn Ezhilarasi I, J. Christopher Clement

{"title":"Robust cooperative spectrum sensing in cognitive radio blockchain network using SHA-3 algorithm","authors":"Evelyn Ezhilarasi I, J. Christopher Clement","doi":"10.1016/j.bcra.2024.100224","DOIUrl":"10.1016/j.bcra.2024.100224","url":null,"abstract":"<div><div>Cognitive radio network (CRN) uses the available spectrum resources wisely. Spectrum sensing is the central element of a CRN. However, spectrum sensing is susceptible to multiple security breaches caused by malicious users (MUs). These attackers attempt to change the sensed result in order to decrease network performance. In our proposed approach, with the help of blockchain-based technology, the fusion center is able to detect and prevent such criminal activities. The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing. The detection strategy takes place in two stages: block updation phase and iron out phase. The simulation results of the proposed method demonstrate 3.125%, 6.5%, and 8.8% more detection probability at −5 dB signal-to-noise ratio (SNR) in the presence of MUs, when compared to other methods like equal gain combining (EGC), blockchain-based cooperative spectrum sensing (BCSS), and fault-tolerant cooperative spectrum sensing (FTCSS), respectively. Thus, the security of cognitive radio blockchain network is proved to be significantly improved.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100224"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Looking for stability in proof-of-stake based consensus mechanisms 寻找基于股权证明的共识机制的稳定性

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100222

Alberto Leporati, Lorenzo Rovida

引用次数: 0

A deep decentralized privacy-preservation framework for online social networks

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100233

Samuel Akwasi Frimpong , Mu Han , Emmanuel Kwame Effah , Joseph Kwame Adjei , Isaac Hanson , Percy Brown

{"title":"A deep decentralized privacy-preservation framework for online social networks","authors":"Samuel Akwasi Frimpong , Mu Han , Emmanuel Kwame Effah , Joseph Kwame Adjei , Isaac Hanson , Percy Brown","doi":"10.1016/j.bcra.2024.100233","DOIUrl":"10.1016/j.bcra.2024.100233","url":null,"abstract":"<div><div>This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. We propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities. Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. Additionally, a blockchain network secures users’ data via smart contracts, ensuring robust data protection. When tested on the NSL-KDD dataset, our framework achieves 98.79% accuracy, a 10% false alarm rate, and a 98.99% detection rate, surpassing existing methods. The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100233"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A prototype model of zero trust architecture blockchain with EigenTrust-based practical Byzantine fault tolerance protocol to manage decentralized clinical trials

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100232

Ashok Kumar Peepliwal , Hari Mohan Pandey , Surya Prakash , Sudhinder Singh Chowhan , Vinesh Kumar , Rahul Sharma , Anand A. Mahajan

{"title":"A prototype model of zero trust architecture blockchain with EigenTrust-based practical Byzantine fault tolerance protocol to manage decentralized clinical trials","authors":"Ashok Kumar Peepliwal , Hari Mohan Pandey , Surya Prakash , Sudhinder Singh Chowhan , Vinesh Kumar , Rahul Sharma , Anand A. Mahajan","doi":"10.1016/j.bcra.2024.100232","DOIUrl":"10.1016/j.bcra.2024.100232","url":null,"abstract":"<div><div>The COVID-19 pandemic necessitated the emergence of Decentralized Clinical Trials (DCTs) due to patient retention, accelerating trials, improving data accessibility, enabling virtual care, and facilitating seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the IoT has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100232"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A blockchain-based platform for incentivizing customer reviews in the grocery industry

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100226

Tania Bruno , Ettore Etenzi , Luca Gualandi , Eraldo Katra , Rosario Pugliese , Alessio Taranto , Francesco Tiezzi

{"title":"A blockchain-based platform for incentivizing customer reviews in the grocery industry","authors":"Tania Bruno , Ettore Etenzi , Luca Gualandi , Eraldo Katra , Rosario Pugliese , Alessio Taranto , Francesco Tiezzi","doi":"10.1016/j.bcra.2024.100226","DOIUrl":"10.1016/j.bcra.2024.100226","url":null,"abstract":"<div><div>Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often, there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing users' creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We develop a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100226"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unraveling the potential of blockchain technology in enhancing supply chain traceability: A systematic literature review and modeling with ISM

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-11-13 DOI: 10.1016/j.bcra.2024.100240

Reza Payandeh , Ahmad Delbari , Fatemeh Fardad , Javad Helmzadeh , Sanaz Shafiee , Ali Rajabzadeh Ghatari

{"title":"Unraveling the potential of blockchain technology in enhancing supply chain traceability: A systematic literature review and modeling with ISM","authors":"Reza Payandeh , Ahmad Delbari , Fatemeh Fardad , Javad Helmzadeh , Sanaz Shafiee , Ali Rajabzadeh Ghatari","doi":"10.1016/j.bcra.2024.100240","DOIUrl":"10.1016/j.bcra.2024.100240","url":null,"abstract":"<div><div>Supply chain traceability is a critical aspect of modern business operations, and blockchain technology has emerged as a promising solution to enhance traceability in supply chain management. However, the effective application of blockchain faces various challenges and limitations. This study aims to investigate how blockchain technology can address these challenges and improve traceability within supply chains. Employing a systematic literature review combined with interpretative structural modeling (ISM), we comprehensively assess and classify the literature on blockchain-enabled supply chain traceability. Our exploratory research approach delves into the contributions of blockchain technology and identifies key factors that enhance traceability. We adopt a mixed-methods approach, incorporating both secondary and primary data to ensure robust analysis. Our study addresses essential questions regarding the application, advantages, limitations, challenges, integration with other technologies, and future potential of blockchain in supply chain traceability. Through a systematic review and the ISM technique, we identify crucial levels and factors necessary for leveraging blockchain technology effectively. Our findings underscore the importance of a robust infrastructure, cutting-edge technology, and significant initial investment in implementing blockchain for supply chain traceability. This research offers a comprehensive understanding of the factors and their levels, providing valuable insights for industry professionals and academic researchers. By laying a solid foundation for informed decision-making and further exploration into the potential of blockchain-enhanced supply chain traceability, our study contributes to advancing knowledge in this crucial area of business operations.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 1","pages":"Article 100240"},"PeriodicalIF":6.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling efficient verification in a DApp: The case of copyright management

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-10-10 DOI: 10.1016/j.bcra.2024.100234

Pierpaolo Della Monica , Matteo Fedeli , Cristina Salonico , Andrea Vitaletti , Marco Zecchini

{"title":"Enabling efficient verification in a DApp: The case of copyright management","authors":"Pierpaolo Della Monica , Matteo Fedeli , Cristina Salonico , Andrea Vitaletti , Marco Zecchini","doi":"10.1016/j.bcra.2024.100234","DOIUrl":"10.1016/j.bcra.2024.100234","url":null,"abstract":"<div><div>The Interested Party Information (IPI) system uniquely identifies the rights holders worldwide, making it possible to know for each subject and at any time which rights are protected, by whom and for which territories. Currently, this service is provided in a centralized way but in 2021, the Italian Society of Authors and Editors (SIAE) deployed a blockchain-based solution to completely decentralize this database to (a) provide greater guarantees to the rights holders as well as end users and (b) make a first tangible step in the path towards an all in-chain solution decentralizing a relevant component of the current architecture. This solution relied on early versions of Algorand smart contracts, delegating some off-chain verification to trusted third parties in many practical scenarios. Moreover, the Algorand technology has developed new tools, allowing us to design new techniques to reduce some of the trust assumptions of the original solution and enhance its efficiency at the same time. In this paper, we present the evolution of the solutions we designed to issue new on-chain non-conflicting rights representations, namely representations that are consistent with those already available on-chain. Our solution relies on smart contracts that have been implemented to run our experiments to prove (a) the feasibility of the proposed approach, (b) the scalability of the proposed solutions, and (c) the sustainability in terms of costs.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 1","pages":"Article 100234"},"PeriodicalIF":6.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis 利用机器学习分类器和可解释性分析检测区块链交易中的异常情况

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100207

Mohammad Hasan , Mohammad Shahriar Rahman , Helge Janicke , Iqbal H. Sarker

{"title":"Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis","authors":"Mohammad Hasan , Mohammad Shahriar Rahman , Helge Janicke , Iqbal H. Sarker","doi":"10.1016/j.bcra.2024.100207","DOIUrl":"10.1016/j.bcra.2024.100207","url":null,"abstract":"<div><div>As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100207"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT 基于双区块链的工业物联网异构数据多层分组联合学习方案

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100195

{"title":"Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT","authors":"","doi":"10.1016/j.bcra.2024.100195","DOIUrl":"10.1016/j.bcra.2024.100195","url":null,"abstract":"<div><p>Federated learning (FL) allows data owners to train neural networks together without sharing local data, allowing the industrial Internet of Things (IIoT) to share a variety of data. However, traditional FL frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposes a dual-blockchain based multi-layer grouping federated learning (BMFL) architecture. BMFL divides the participant groups based on the training tasks, then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault datasets. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.</p></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100195"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096720924000083/pdfft?md5=17c58876f9db0cb915d1b0b20cbe64c3&pid=1-s2.0-S2096720924000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140468200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An interpretable model for large-scale smart contract vulnerability detection 大规模智能合约漏洞检测的可解释模型

IF 6.9 3区计算机科学

Blockchain-Research and Applications Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100209

{"title":"An interpretable model for large-scale smart contract vulnerability detection","authors":"","doi":"10.1016/j.bcra.2024.100209","DOIUrl":"10.1016/j.bcra.2024.100209","url":null,"abstract":"<div><div>Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lack of interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features, thereby reducing the scales of features. The learning process could be accelerated significantly because of the reduction of the feature size. The experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each type is higher than 93% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerability is reduced by nearly 80% compared with that of its original.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 3","pages":"Article 100209"},"PeriodicalIF":6.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141390225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0