As the cryptocurrency market continues to evolve, phishing scams are considered one of the most deceptive forms of fraud. Currently, most existing Ethereum phishing detection methods rely on traditional machine learning or graph representation learning, mainly depending on local statistical and structural features. This can lead to insufficient utilization of transaction graph data across different scales. To address this challenge, we propose Multi-transaction-view Graph Attention Network (MTvGAT), which fully leverages edge features between nodes at different scales and discovers relationships between nodes. Two types of graphs are used to model Ethereum transactions: global views and local views. Global views are constructed by partitioning the complete transaction graph using graph clustering algorithms and inputting them into MTvGAT to obtain global view representations. For each target node, a local view is constructed by sampling K-hop neighbors from the transaction network. Importantly, attention coefficients are calculated between nodes, and edge coefficients are obtained by fusing edge features and attention coefficients, utilizing spatial structure and edge coefficients to enable the phishing detection model to access multi-view sources of information. Experimental results demonstrate that the multi-view graph attention network outperforms existing algorithms in detecting Ethereum phishing scams datasets.
Cryptocurrency, a decentralized digital currency, has brought about significant social impacts. This essay explores its effects on crime rates, global economic integration, and the GPU market while also delving into the underlying technologies of blockchain and cryptography. The introduction introduces the concept of cryptocurrency as a decentralized, pseudo-anonymous digital currency. The historical journey and the impacts of cryptocurrency are discussed in section 2, focusing on its social implications. The technological underpinnings are further discussed in section 3. The paper concludes by addressing the core technologies: cryptography and blockchain. Cryptography ensures security and anonymity, with SHA-256 as a fundamental algorithm. Blockchain, a decentralized database, interlinks transaction blocks, making tampering difficult due to Proof-of-Work validation. In essence, cryptocurrencyโs social impacts and technological foundations intersect, offering insights into its intricate landscape.
In todayโs highly interconnected digital environment, computer network information security faces multiple threats such as data breaches, identity forgery, and malicious attacks. As a decentralized and tamper-proof distributed ledger system, blockchain technology provides a new technical path for data security through its core features of cryptographic algorithms and consensus mechanisms. This technology can ensure the integrity of information during data transmission and storage, effectively enhance the overall credibility of network systems, and inject new vitality into the information security protection system. Based on this, this paper explores the implementation of blockchain technology in computer network information security.
Bitcoin, as the most widely recognized cryptocurrency, has attracted significant global attention from businesses, consumers, and investors. This study introduces a hybrid model that integrates an online self-learning stochastic configuration network (OSL-SCN) with conformal prediction. The model autonomously adjusts its parameters in response to real-time data.Predictions from the OSL-SCN are refined through conformal prediction, which generates confidence intervals to enhance reliability. The results, using historical Bitcoin prices from Wikipedia, demonstrate that the combined approach enhances both prediction accuracy and reliability.
Cryptocurrency being a digital or virtual currency that uses cryptography to secure transactions and control the creation of new units. Bitcoin, one of the most popular cryptocurrency, offers various advantages such as security, transparency, and efficiency. The value of Bitcoin can change over time, similar to the regular currencies, and the need to predict the value can be as important as those in the regular. The prediction can be done by multiple algorithms. The purpose of this research is to compare five algorithms in predicting bitcoin value based on Root Mean Squared Error (RMSE) and Squared Error (R2). The five algorithms compared can model the prediction of changes in the bitcoin cryptocurrency, effectively. Based on the experiment, Random Forest outperformed the other algorithms based on its RMSE and R2 result
The Internet of Things (IoT) has emerged as a promising technology for connecting physical objects and enabling communication between them. However, IoT faces several challenges such as security, privacy, and interoperability that need to be addressed before large-scale deployment. Blockchain has the potential to address these challenges due to its decentralized architecture, cryptographic security, immutability and consensus mechanisms. This paper explores the integration of blockchain technology with IoT for industrial applications. A cross-chain architecture is proposed to build a high-performance blockchain network customized for the needs of Industrial IoT (IIoT). The limitations of existing blockchain platforms like low transaction throughput are overcome by improvements in the consensus algorithm and employing a multi-chain structure. A proof-of-stake based consensus is implemented to achieve fast consensus and improve scalability. Cross-chain communication protocols are designed to enable interoperability between heterogeneous blockchains. The experimental evaluation demonstrates that the resulting blockchain network can achieve over 1000 transactions per second throughput per chain while maintaining decentralization, security and low latency. The cross-chain structure enables this performance to scale as the network expands. The paper provides comprehensive technical details of the various components of the blockchain platform including consensus, cross-chain communication and network structure. The designed blockchain architecture can potentially enable several IIoT use cases by enhancing security, accessibility, automation and coordination of industrial systems.
Zhaohui Guo, Qiang Liu, Zhen Gao, Lei Liu ยท 8 authors
Asan emerging distributed ledger technology, blockchain provides multi-party trust between unreliable devices to share data and resources cooperatively, which facilitates the Internet of Vehicles (IoVs) applications. In the typical blockchain-enabled IoV (BIoV) scenarios, based on the data of common interest collected by various sensors, the IoV devices jointly maintain the world state in the form of address-balance pairs as the proof for transaction issuing and validation, where Ethereum-like blockchain is used as the finite-state machine driven by the transactions. However, with the extension of the IoV network, an enormous number of accounts leads to the explosive growth of the state data, which has been the main challenge for BIoV with resource-limited devices. This paper proposes a modular-based adaptive bit-width compression (ABC) scheme to reduce the state data storage on each device by representing the address as a remainder with a shorter bit-width. Besides, a new transaction validation method is designed with the support of the XOR filter, which guarantees that the core functions of the blockchain can still be performed normally with the proposed scheme applied. Theoretical analysis and simulation results show that the compression ratio for the address data could be more than 80%, which dramatically improves the scalability of BIoV system. In addition, the extra privacy-preserving property is introduced with the compression scheme because the account information is unrecoverable from the remainders.
Smart healthcare systems play a pivotal role in delivering accessible medical services, especially in remote environments where patients rely on wearable devices to collect medical data, subsequently transmitted to caregivers for diagnosis purpose. Despite the benefits of telehealth systems, their vulnerability to security breaches stemming from insecure communication channels highlights the critical need for robust mechanisms ensuring secure data transmission and access control. These mechanisms are essential to verify the legitimacy of both the patients and caregivers. To address these challenges, we propose ZKP-MAC, a lightweight Zero Knowledge proof-based authentication and access control scheme specifically tailored for Telehealth systems. Our proposed scheme integrates physical unclonable functions (PUFs), leveraging cryptographic keys derived from device hardware fingerprints to authenticate patient data. Additionally, we adopt a zero-knowledge proof protocol for caregivers' authentication and access control. By regenerating keys based on device hardware fingerprints, our approach ensures lightweight, secure, and authentic communication. Additionally, our model enables dynamic key management mechanism to regulate data access effectively and continuously in telehealth applications. Experimental results demonstrate the robustness of our approach against various security threats, offering features such as perfect backward secrecy and mutual access control. We conduct a comprehensive evaluation of the ZKP-MAC's security attributes, and computational overhead, showcasing reduced complexity compared to competing schemes while maintaining resilience against attempts to compromise security features by both individual and colluding actors. Furthermore, our scheme undergoes rigorous formal analysis using Automated Verification of Internet Security Protocols and Applications (A VISP A).
The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from usersโ resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithmโs efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
With the rapid development of the Internet, the problem of network information security has become increasingly prominent. The traditional network security solutions have the weaknesses of centralization and vulnerability to attack, and cannot effectively deal with the risk of attack and data tampering. As a decentralized and tamper-resistant distributed ledger technology, blockchain technology has a good potential to be applied to network information security protection. This paper aims to summarize the basic principle and security characteristics of blockchain technology, discuss the network information security protection method based on blockchain, and analyze the application of blockchain technology in identity protection, food supply chain, O2O catering and other aspects for reference.
Blockchain technology can create a shared platform for English translation and reserve a large number of practical corpus resources, thus improving the quality of machine translation. This paper first introduces the research status of foreign language corpus and blockchain English translation in China. Then it introduces the basic principles of BPNN and particle swarm optimization and constructs the PSO-BP model. Experiments show that the prediction accuracy of BPNN optimized by particle swarm optimization algorithm is greatly improved, the convergence speed is faster, and it will not fall into the local optimal trap. Finally, this paper proposes the implementation path of blockchain in corpus translation application: (1) build โblockchain+ AIโ English translation corpus and (2) improve the machine English translation software of the โblockchain+ AIโ English translation training platform.
This study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
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, Shapleyโฒs 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.
The recent revolution in Industry 4.0 (IR 4.0) has characterized the integration of advance technologies to bring the fourth industrial revolution to scale the manufacturing landscape. There are different key drivers for this revolution, in this research we have explored the following among them such as, Industrial Internet of Things (IIoT), Deep Learning, Blockchain and Augmented Reality. The emerging concept from blockchain namely โNon-Fungible Tokenโ (NFT) relating to the uniqueness of digital assets has vast potential to be considered for physical assets identification and authentication in the IR 4.0 scenario. Similarly, the data acquired through the deployment of IIoT devices and sensors into smart industry spectrum can be transformed to generated robust analytics for different industry use-cases. The predictive maintenance is a major scenario in which early equipment failure detection using deep learning model on acquired data from IIoT devices has major potential for it. Similarly, the augmented reality can be able to provide real-time visualization within the factory environment to gather real-time insight and analytics from the physical equipment for different purposes. This research initially conducted a survey to analyse the existing developments in these domains of technologies to further widen its horizon for this research. This research developed and deployed a smart contract into an ethereum blockchain environment to simulate the use-case for NFT for physical assets and processes synchronization. The next phase was deploying deep learning algorithms on a dataset having data generated from IIoT devices and sensors. The Feedforward and Convolutional Neural Network were used to classify the target variables in relation with predictive maintenance failure analysis. Lastly, the research also proposed an AR based framework for the visualization ecosystem within the industry environment to effectively visualize and monitory IIoT based equipmentโs for different industrial use-cases i.e., monitoring, inspection, quality assurance.
The demand for the Internet of Everything has slowed down network routing efficiency. Traditional routing policies rely on manual configuration, which has limitations and adversely affects network performance. In this paper, we propose an Internet of Things (IoT) Intelligent Edge Network Routing (ENIR) architecture. ENIR uses deep reinforcement learning (DRL) to simulate human learning of empirical knowledge and an intelligent routing closed-loop control mechanism for real-time interaction with the network environment. According to the network demand and environmental conditions, the method can dynamically adjust network resources and perform intelligent routing optimization. It uses blockchain technology to share network knowledge and global optimization of network routing. The intelligent routing method uses the deep deterministic policy gradient (DDPG) algorithm. Our simulation results show that ENIR provides significantly better link utilization and transmission delay performance than various routing methods (e.g., open shortest path first, routing based on Q-learning and DRL-based control framework for traffic engineering).
J. D. Preece, Christopher J. Morris, John M. Easton
The System for Ticketing Ubiquity with Blockchains (STUB) is a novel solution to multi-modal transport ticketing. Introduced previously using Hyperledger Fabric, STUB utilises the distributed mechanics of blockchain technology right at the core of its architecture, allowing stakeholders from different transport modes to vend and validate tickets on a shared ledger. This open approach to ticketing data will benefit transport governing bodies, transport operators, and passengers alike by ensuring cross-party cooperation and presenting a fresh holistic approach to the ticketing sector. This paper addresses issues from STUB 1.0, concerning validating tickets for a multi-modal transport system. To overcome this, we propose creating a graph structure, known as the Transport Network Graph (TNG), to represent the transport network with all of the services provided by the Transport Service Providers (TSPs). This enables the implementation of an automated Revenue Allocation System (RAS), whilst retaining the benefits provided by blockchain technology.
Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a "Proof of Learning" consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.
This research aims to compare the performance between the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) methods in predicting the Bitcoin exchange rate against the US Dollar (BTC-USD). The data used comes from Yahoo Finance for the period 2017-2022. Each model is built with a comparable architecture and evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (Rยฒ), and prediction accuracy metrics. The results show that the LSTM model performed better on the test data with a MAPE of 3.80% and an accuracy of 96.20%, while the GRU model achieved a MAPE of 5.13% and an accuracy of 94.87%. Although the GRU model performed better on the training data, the LSTM model showed better generalization ability on the testing data. This research provides important insights into the selection of the optimal recurrent neural network architecture for Bitcoin exchange rate prediction which is known for its high volatility.
Blockchain products are more and more widely used. How to reasonably evaluate blockchain products has become a hot issue. The main work of this paper is to establish a set of general evaluation indicators for blockchain products, and analyze the needs of future software systems. Firstly, the paper analyzes the common five-tier architecture adopted by the current blockchain system, namely data layer, network layer, consensus layer, smart contract layer and application layer, and expounds the hierarchical characteristics and technical contents of each layer in detail; Then on this basis, a set of general evaluation indicators is proposed for the current common blockchain products, in which the evaluation indicators can be divided into six items: distributed ledger evaluation indicators, public key password evaluation indicators, point-to-point network technology evaluation indicators, consensus mechanism evaluation indicators, intelligent contract mechanism evaluation indicators and upper layer application evaluation indicators. The establishment of indicators can comprehensively evaluate the availability, security and system performance of blockchain products. Finally, based on the evaluation index, the functional and non functional analysis of the blockchain product evaluation system is carried out, which lays a good foundation for the realization of software in the future. The design of evaluation indicators and the functional analysis of the evaluation system will promote the standardization of blockchain products.
Tie Qiu, Min Zhang, Xize Liu, Jing Liu ยท 6 authors
As the application of the industrial Internet of Things (IIoT) becomes more widespread, the IIoT is being combined with social networks. Nodes in the network can be users, machines, and so on. Using the sensing detection technology of the IIoT, industrial machines can realize real-time informatization, which is convenient for users to perform remote management. Nodes can communicate with each other and make ratings. These ratings can be modeled as directed weighted edges between nodes and form directed weighted networks (DWNs). The edge weight represents the โstrengthโ of relationship and the direction of edge points from the edge generator to the edge receiver. Predicting edge weights in DWNs is critical to predicting unknown ratings or recovering lost data. In this article, we propose a directed edge weight prediction model (DEWP) using decision tree ensembles. It extends the local similarity indices to DWNs and extracts a series of similarity indices between nodes as features of each edge. These features are used to construct a blended regression model of random forest, gradient boost decision tree, extreme gradient boosting, and light gradient boosting machine. The proposed algorithm was evaluated experimentally with the Bitcoin OTC and Bitcoin Alpha datasets by removing 10% to 90% of edges in the original network. Compared with other classical algorithms, DEWP has higher prediction accuracy and robustness.
This paper researches the consensus mechanism of Ethereum, focusing on the analysis and research of the consensus algorithm. The consensus algorithm is the kernel of Ethereum implementation, which is the most computationally intensive, time-consuming and occupies much memory. The concrete implementation architecture and method of heterogeneous operation are analyzed and implemented on the Xilinx ZCU106 platform. Finally, the verification content has been obtained.