Blockchain-Research and Applications最新文献

The recent surge of Ethereum in prominence has made it an attractive target for various kinds of crypto crimes. Phishing scams, for example, are an increasingly prevalent cybercrime in which malicious users attempt to steal funds from a user's crypto wallet. This research investigates the effects of network architectural features as well as the temporal aspects of user activities on the performance of detecting phishing users on the Ethereum transaction network. We employ traditional machine learning algorithms to evaluate our model on real-world Ethereum transaction data. The experimental results demonstrate that our proposed features identify phishing accounts efficiently and outperform the baseline models by 4% in Recall and 5% in F1-score.

The valuation of cryptocurrencies is important given the increasing significance of this potential asset class. However, most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources. In this study, a robust cryptocurrency valuation method is developed using up-to-date datasets. Using various panel regression models and moving-window regression tests, the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1, 2009 to April 30, 2023. The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment, while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency. Moreover, consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins, while their impacts on cryptocurrencies with stablecoins are insignificant. Other fundamental factors, such as the type of supply and the presence of smart contracts, do not have a significant influence on cryptocurrency. Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors, portfolio managers, and policymakers in assessing the utility level of each cryptocurrency.

Federated Learning (FL) is a type of distributed deep learning framework in which multiple devices train a local model using local data, and the gradients of the local model are then sent to a central server that aggregates them to create a global model. This type of framework is ideal where data privacy is of utmost importance because the data never leave the local device. However, a major concern in FL is ensuring the data quality of local training data. Since there is no control over the local training data, ensuring that the local model is trained on clean data becomes challenging. A model trained on poor-quality data can have a significant impact on its accuracy. In this paper, we propose a decentralized approach using blockchain to ensure local model data quality. We use miners to validate each local model by checking its accuracy against a secret testing dataset. This is done using a smart contract that the miners invoke during the mining process. The local model is aggregated with the global model only if it passes a preset accuracy threshold. We test our proposed method on two datasets: the Brain Tumor Classification dataset from Kaggle, comprised of 7000 MRI images divided into two classes (Tumor/No Tumor), and the Medical MNIST dataset, which includes 58,954 images classified into six different classes: AbdomenCT, BreastMRI, ChestCT, Chest X-ray, Hand X-ray, and HeadCT. Our results show that our method outperforms the original FL approach in all experiments.

The rapid growth of the Internet of Things (IoT) has raised security concerns, including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security (TLS) protocols. This paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining efficiency. This approach involves using blockchain sharding, which enables higher scalability, improved performance, and reduced computational overhead compared to traditional blockchain approaches, making it well-suited for resource-constrained IoT environments. This approach leverages Ethereum blockchain's smart contract mechanism to ensure trust, accountability, and user privacy. Specifically, we introduce a shard-based consensus mechanism that enables improved security while minimizing computational overhead. We also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain network. The proposed approach is analyzed for usability, including metrics such as bandwidth consumption, CPU usage, memory usage, delay, access time, storage time, and jitter, which are essential for IoT application requirements. The analysis demonstrated that the approach reduces resource consumption, and the proposed system outperforms TLS and existing blockchain approaches in these metrics, regardless of the choice of the MQTT broker. Additionally, thoroughly addressing future research directions, including issues and challenges, ensures careful consideration of potential advancements in this domain.

Since the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism can avoid the performance bottleneck of blockchain systems caused by Proof of Work (PoW), it has been widely used in many scenarios. However, in the blockchain system, each node is required to back up all transactions and block data of the system, which will waste a lot of storage resources. It is difficult to apply to scenarios with limited storage resources such as unmanned aerial vehicle networks and smart security protection; thus, it is often used in small-scale networks. In order to deploy PBFT-based blockchain systems in large-scale network scenarios, we propose an ultra-low storage overhead PBFT consensus (ULS-PBFT), which groups nodes hierarchically to limit the storage overhead within the group. In this paper, we first propose an optimal double-layer PBFT consensus from the perspective of minimizing the storage overhead, and prove that this consensus can significantly reduce the storage overhead. In addition, we also investigate the superiority of ULS-PBFT in terms of communication overhead while setting the security threshold in the presence of the possibility of Byzantine nodes. The simulation results demonstrate the advantages of ULS-PBFT. Then, we extend such grouping idea to the blockchain system with X-layer PBFT and analyze its storage and communication overhead. Finally, the node grouping strategy of double-layer PBFT is studied for four application scenarios when the performance of storage overhead, communication overhead, and security are considered comprehensively.

The modern pharmaceutical supply chain lacks transparency and traceability, resulting in alarming rates of counterfeit products entering the market. These illegitimate products cause harm to end users and wreak havoc on the supply chain itself, costing billions of dollars in profit loss. In this paper, in response to the Drug Supply Chain Security Act (DSCSA), we introduce Janus, a novel pharmaceutical track-and-trace system that utilizes blockchain and cloning-resistant hologram tags to prevent counterfeits from entering the pharmaceutical supply chain. We design a multi-quorum consensus protocol that achieves load balancing across the network. We perform a security analysis to show robustness against various threats and attacks. The implementation of Janus proves that the system is fair, scalable, and resilient.

Blockchain is a revolutionary technology that has the potential to revolutionize various industries, including finance, supply chain management, healthcare, and education. Its decentralized, secure, and transparent nature makes it ideal for use in industries where trust, security, and efficiency are of paramount importance. The integration of blockchain technology into the education system has the potential to greatly improve the efficiency, security, and credibility of the educational process. By creating secure and transparent platforms for tracking and verifying students' academic achievements, blockchain technology can help to create a more accessible and trustworthy education system, making it easier for students to showcase their skills and knowledge to potential employers. While the potential benefits of blockchain in education are significant, there are also several challenges that must be addressed in order to fully realize the potential of this technology in the educational sector. Some of the major challenges include adoption, technical knowledge, interoperability, regulation, cost, data privacy and security, scalability, and accessibility. The necessary equipment for the implementation of blockchain technology in education is diverse and critical to the success of this innovative technology. Organizations should carefully consider this equipment when planning their implementation of blockchain technology in education to ensure the efficient and secure transfer of educational data and transactions within the blockchain network. Blockchain technology has the potential to play a significant role in promoting sustainability education and advancing the sustainability goals of both individuals and organizations. Organizations should consider incorporating blockchain technology into their sustainability education programs, in order to enhance the transparency, verifiability, and efficiency of their sustainability-related activities. While the use of blockchain technology in education is still in its early stages, the available data suggest that it has significant potential to transform the education sector and improve the efficiency and transparency of educational systems.

Smart contracts (SCs) are crucial in maintaining trust within blockchain networks. However, existing methods for analyzing SC vulnerabilities often lack accuracy and effectiveness, while approaches based on Deep Neural Networks (DNNs) struggle with detecting complex vulnerabilities due to limited data availability. This paper proposes a novel approach for analyzing SC vulnerabilities. Our method leverages an advanced form of the Genetic Algorithm (GA) and includes the development of a comprehensive benchmark dataset consisting of 36,670 Solidity source code samples. The primary objective of our study is to profile vulnerable SCs effectively. To achieve this goal, we have devised an analyzer called SCsVulLyzer based on GAs, designed explicitly for profiling SCs. Additionally, we have carefully curated a new dataset encompassing a wide range of examples, ensuring the practical validation of our approach. Furthermore, we have established three distinct taxonomies that cover SCs, profiling techniques, and feature extraction. These taxonomies provide a systematic classification and analysis of information, improving the efficiency of our approach. Our methodology underwent rigorous testing through experimentation, and the results demonstrated the superior capabilities of our model in detecting vulnerabilities. Compared to traditional and DNN-based approaches, our approach achieved higher precision, recall, and F1-score, which are widely used metrics for evaluating model performance. Across all these metrics, our model showed exceptional results. The customization and adaptations we implemented within the GA significantly enhanced its effectiveness. Our approach detects SC vulnerabilities more efficiently and facilitates robust exploration. These promising results highlight the potential of GA-based profiling to improve the detection of SC vulnerabilities, contributing to enhanced security in blockchain networks.

We propose a new approach for a secure, decentralized, and censorless upgrade of existing cryptocurrencies to newly created tokens without interaction with any external information sources (oracles). The proposed scheme is based on the burning of existing cryptocurrency tokens and implemented via the multi-currency auction. The auction is carried out on the blockchain of the new token and implemented using a smart contract that processes participants' bids of burnt tokens of other cryptocurrencies and supports a new token price discovery algorithm for each cryptocurrency with no oracles or any other trusted source of information. Contrary to traditional ways of getting the new asset, like centralized and decentralized exchanges, our method requires no user registration (as well as no KYC — “know your customer” procedure that requires obligatory client identification) and provides a predicted supply level of the new asset for an adequate price within a model with economically rational participants. We provide the results of decentralized auction simulations implemented for several strategies of user behavior (based on bid prices with normal and log-normal distribution laws), both under the normal operation and in the presence of an adversary who follows specific strategies.