Blockchain-Research and Applications最新文献

In today's corporate environment, Environmental, Social, and Governance (ESG) reports crucially reflect an organization's commitment to sustainability, environmental preservation, and social responsibility. As corporations share these detailed reports, the responsibility to validate and assure adherence to respected ESG benchmarks critically lies with third-party assurance organizations. However, the essential verification process often encounters challenges related to authenticity, credibility, and fairness, underscoring the need for a new solution. The selection of verifiers is a crucial aspect of this process, as their expertise and impartiality directly impact the validity and trustworthiness of the verification. Consequently, “Veri-Green,” an innovative blockchain-based incentive mechanism, has been introduced to improve the ESG data verification process. Considering potential risks in verification systems, such as reputational damage due to oversight or inadvertent approval of inaccurate data, and data security risks involving the management of sensitive organizational information, the verifier selection process needs to be thoroughly considered and designed. Through the utilization of advanced machine learning algorithms, potential verification candidates are precisely identified, followed by the deployment of the Vickrey Clarke Groves (VCG) auction mechanism. This approach ensures the strategic selection of verifiers and cultivates an ecosystem marked by truthfulness, rationality, and computational efficiency throughout the ESG data verification process. In this framework, verifiers are not only encouraged but also properly incentivized, developing a more transparent and equitable verification process, thereby driving the ESG agenda towards a future defined by genuine, impactful corporate responsibility and sustainability.

Blockchain-based cryptocurrencies, such as Bitcoins, are increasingly popular. However, the decentralized and anonymous nature of these currencies can also be (ab)used for nefarious activities such as money laundering, thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors. In this paper, we propose TMAS, a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies. Specifically, the proposed system includes ten features in the transaction graph, two heuristic money laundering models, and an analysis method for account linkage, which identifies accounts that are distinct but controlled by an identical entity. To evaluate the effectiveness of our proposed indicators and models, we analyze 100 million transactions and compute transaction features, and are able to identify a number of suspicious accounts. Moreover, the proposed methods can be applied to other cryptocurrencies, such as token-based cryptocurrencies (e.g., Bitcoins) and account-based cryptocurrencies (e.g., Ethereum).

The proliferation of Internet of Things (IoT) devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased security. We argue that digital twins of IoT devices, with the right design, can enhance the security, reliability, auditability, and interoperability of IoT systems. The salient features of digital twins have made them key elements for the IoT and Industry 4.0. In this paper, we leverage advances in W3C's Web of Things (WoT) standards and distributed ledger technologies (DLTs) to present a novel design of the smart contract-based digital twins with enhanced security, transparency, interoperability, and reliability. We provide two different variations of that general design using two different blockchains (one public and one private, permissioned blockchain), and we present design trade-offs. Furthermore, we introduce an architecture for accessing and controlling IoT devices securely and reliably, providing full auditability, while at the same time using the proposed digital twins as an indirection mechanism (proxy). The proposed architecture leverages the blockchain to offer notable properties, namely,decentralization, immutability, auditability, non-repudiation, availability, and reliability. Moreover, it introduces mass actuation, easier management of IoT devices, and enhanced security to the IoT gateways, enables new business models, and makes consumer devices (vendor-)agnostic.

Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using blockchain and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a blockchain prevention module and an ML detection module. The blockchain prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.

Web3, the next generation of the Internet, represents a decentralized and democratized web. Although it has garnered significant public interest and found numerous real-world applications, there is a limited understanding of people's perceptions and experiences with Web3. In this study, we conducted an empirical study to investigate the categories of Web3 applications and their popularity, as well as the potential challenges and opportunities within this emerging landscape. Our research was carried out in two phases. In the first phase, we analyzed 200 popular Web3 projects associated with 10 leading Web3 venture capital firms. In the second phase, we collected and examined code-related data from GitHub and market-related data from blockchain browsers (e.g., Etherscan) for these projects. Our analysis revealed that the Web3 ecosystem can be categorized into two groups, i.e., Web3 infrastructure and Web3 applications, with each consisting of several subcategories or subdomains. We also gained insights into the popularity of these Web3 projects at both the code and market levels and pointed out the challenges in the Web3 ecosystem at the system, developer, and user levels, as well as the opportunities it presents. Our findings contribute to a better understanding of Web3 for researchers and developers, which in turn promotes further exploration and advancement in this innovative field.

In recent years, there has been a growing interest in real estate investments that utilize blockchain technology. Traditional real estate investments usually involve third-party intermediaries for verifying and recording informal real estate transactions. This paper proposes a blockchain-based real estate investment model and presents a detailed description of the real estate register authentication aspect of the model. The model uses blockchain technology to create tamper-evident records of real estate transactions and provide secure authentication and verification for informal real estate transactions. Meanwhile, each real estate transaction is recorded in a block, and all transaction records are kept on the blockchain. This means that inventors can access these transaction records and verify their authenticity and validity. The system can also use smart contracts to automate the process of real estate transactions, which further improves transaction efficiency and reduces costs. Furthermore, the model's timestamp and authentication mechanism can eliminate third-party intermediaries and ensure the authenticity and validity of real estate transactions through distributed ledgers and verification mechanisms. Overall, blockchain-based real estate systems offer advantages of security, transparency, efficiency, and cost reduction. With ongoing blockchain advancements, these systems are expected to play a crucial role in future real estate investment transactions.

Opportunistic networks (OppNets) are usually a set of smart, wearable, and portable devices or entities with mobility that connect wirelessly without requiring infrastructure. Such a network is of great importance in data transmission, particularly in incidents and disasters, whether man-made or natural. However, message integrity and confidentiality are of concern when dealing with vital and physiological data transmission under strict privacy regulations. In this work, we propose a structure to classify messages based on their priority in different queues. Furthermore, due to the decentralized architecture of OppNets, we propose a blockchain-based structure for providing security for high-priority messages. It contains three sequences of functional blocks with a light and simplified implementation that make it suitable for battery-powered wearable devices that are limited in energy consumption and computational units. The simulation results show that by increasing the number of nodes in the network, the average of the changes in block sizes is neglectable, which addresses the computation bottleneck. Furthermore, we analyze the performance of the proposed structure in terms of message delivery and network overhead compared with the Epidemic and Prophet routing algorithms. These results indicate advancing the overall performance of the proposed algorithm.

As the global population continues to grow, the enormous stress on our environment and resources is becoming impossible to ignore. A focus on producing and consuming as cheaply as possible has created an economy in which objects are briefly used and then discarded as waste, featuring a linear lifecycle that creates an enormous amount of waste. The alternative to the linear economy “take-make-waste” is called the “circular economy”. Under this paradigm, materials are recycled to build new products or components that are designed and built to promote their reuse and refurbishment. This assures the continuous (re-)exploitation of existing resources, reducing the extraction of new raw materials. However, customers often reject these reused or refurbished products under the suspicion that they do not meet the same usability, safety, or performance levels of new products. In this sense, trustworthy records of historical details of refurbished products could increase consumers’ confidence in products and components of the “circular economy”, prioritizing trustworthiness, reliability, and transparency. This work presents a new certification tool based on blockchain technology to guarantee trusted, accurate, transparent, and traceable lifecycle information of products and their components and to generate trustworthy certificates to probe refurbished product historical details. This tool aims to enhance refurbished product visibility by creating the basis for making the circular economy a reality in any domain.

As the use of artificial intelligence (AI) grows within the field of cybersecurity, so does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of blockchain technology with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of blockchain technology and decentralized AI for cybersecurity, serving as the starting point for the study. This paper begins with an overview of blockchain technology and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between blockchain technology and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled, decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.

Non-fungible token (NFT) is a digital asset whose ownership can be validated and controlled via blockchain technology. The NFT market is a rapidly growing field, and the rarity of NFT is an essential factor that affects its price, as scarcity leads to higher demand. This study focuses on the BAYC NFT collection, which is a successful and representative collection of profile picture NFT, and analyzes how rarity affects NFT prices. This paper investigates the relationship between the rarity and price of the BAYC NFT collection using formal concept analysis (FCA) method. The results show that rarity is a major factor influencing the price of NFT and the effect is more apparent in the medium rarity range. When rarity is very high or very low, other factors become significant determinants, such as the uniqueness and appeal of NFT, and even the naturalness of NFT images. This research highlights the importance of considering rarity when assessing NFT and underscores the need for a comprehensive evaluation of NFT rarity. This study also provides valuable insights into the NFT market and can be useful for NFT investors, creators, and collectors. Furthermore, the usefulness of FCA as a tool for quantifying NFT rarity and evaluating NFT price was demonstrated.