IET Blockchain最新文献

In the current Internet era, whether the domain name system (DNS) can achieve efficient resolution is of great significance for optimizing user experience and promoting global development. However, the existing DNS architectures have flaws in dealing with network service latency and ensuring their own robustness. Based on this, this study constructs a blockchain-based distributed domain master architecture, aiming to optimize the resolution effect of top-level domains (TLDs). As internet service latency continues to pose severe challenges to user experience and global development, the demand for accelerated DNS resolution always exists. Although local caching has always been helpful in improving DNS efficiency, how to ensure the integrity of cached records is a tricky problem. The new architecture proposed in this study adopts a consortium blockchain framework, allowing nodes to freely enter and exit. Consensus is reached through the 3R-PBFT algorithm, strengthening system security. Focusing on the top-level domains of.com and.net, an in-depth exploration is conducted on the effect of locally deployed caches on resolution time, highlighting the key value of TLD caches in enhancing the DNS resolution speed. Experimental results show that the local cache implemented through the consortium blockchain architecture can not only improve the security of the cache but also reduce the latency of DNS resolution.

The distributed denial of service (DDoS) attacks represent a real danger on cloud computing systems, the existing security approaches have notable limitations such as centralization, lack of adaptability and scalability, and focusing on detection over mitigation. In this paper, we present a novel blockchain-based system to enhance attack tolerance of cloud systems against DDoS attacks by integrating a hierarchical risk-based cloud attack tolerance mechanism with smart contracts, The proposed approach ensures comprehensive threat detection and mitigation by estimating risks at all three layers: local, cluster, and global dynamically using Dempster–Shafer theory. Simulations validated the superiority of our approach compared to similar systems in terms of availability, attack detection rate, and packet loss metrics in large-scale cloud simulation of 10,000 nodes. The results demonstrate that our system not only improves security but also maintains service quality under high-intensity attack scenarios. Additionally, the implementation of our blockchain architecture shows significant scalability and cost-efficiency, thus, confirming its suitability for large cloud deployments. Furthermore, the reliability of the smart contracts against potential threats were confirmed by a formal security analysis using the Echidna tool.

As a disruptive technology, blockchain has attracted widespread attention from academia and industry. However, current blockchain technologies still suffer from many severe problems, for example, insufficient trust and security, vulnerability to attacks, low efficiency, poor scalability, low throughput, high energy consumption, privacy leakage, and the inability to remove dishonest or harmful information once it has been recorded. Hence, blockchain evolution is urgently demanded, including its fundamental architecture, consensus mechanism, and new applications. The recent advances in artificial intelligence have offered opportunities to detect anomalies and optimise resources. The evolution of blockchain in architecture, intelligence, and consensus protocol will further extend its applications to broader fields. The current special issue is focused on research ideas, articles and experimental studies related to ‘Blockchain Evolution: Architecture and Applications’ for learning, analysing, and forecasting the various aspects of blockchain.

In this special issue, we have received 12 papers, all of which underwent peer review. Of the 12 originally submitted papers, three have been accepted and nine have been ‘rejected with referral,’ that is, they did not meet the IET Blockchain criteria for publication. Thus, the overall submissions were of high quality, which marks the success of this special issue.

The three eventually accepted papers can be clustered into three main categories, namely theoretical, applications and case studies. The papers in the first category introduce innovations in Ethereum cost optimisation in Layer 2 rollups via EIP-4844, including gas efficiency and economic analysis, to provide solutions for gas costs and system design. The paper in this category is by Dyade et al. The second category of paper designs a system for the blockchain-based intelligent disbursement in national scholarship portal. The paper in this category is by Samu et al. The last category proposes a case study for the adoption of blockchain-based cryptocurrency as a payment method in Saudi Arabia. The paper in this category is by Alrehaili et al. A brief presentation of each of the papers in this special issue follows.

Dyade et al. introduce a comprehensive gas efficiency and economic analysis of EIP-4844's impact on Layer 2 rollups, providing mathematical models, simulation results and architectural diagrams to illustrate the improvements in gas costs and system design. EIP-4844 is poised to drive significant improvements in the efficiency, performance and cost-effectiveness of Ethereum Layer 2 rollups, paving the way for a more scalable and economically viable blockchain ecosystem. Access the full paper using the following link: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/blc2.70014

Samu et al. propose a blockchain-based scholarship module to address these challenges and automate the entire scholarship pro

This paper introduces an architecture for a next-generation airline system, utilising cloud-based microservices, distributed Artificial intelligence (AI), and blockchain to overcome critical system limitations of scale, integrity of data, and avenues of customer inefficiency. In the modular architecture, reservations, payments, and customer profiles can be managed independently, thereby improving scalability by 40% and availability by 30%. AI modules analyse historical data of bookings and user behaviours for demand forecasting and for making personalised recommendations, which, in turn, increases customer engagement by 25%. Blockchain provides proper secure tamper-proof record-keeping of transactions, thereby minimising fraud and increasing data transparency by 20%. The proposed system was evaluated under real-world traffic occurrences, simulating concurrent users in the range of 100–1000, employing a simulation platform that was built for this purpose. The proposed approach reduces transaction latency by 15% and offers a 35% enhancement in throughput for secure data as compared to the usual Systems - Ablation confirms that each module (AI, blockchain, microservices) contributes uniquely to the performance of the system. This architecture holds cross-domain potential, especially for logistics and hospitality. The findings emphasise the transformation possible when we use AI, blockchain, and cloud services in mission-critical, high-demand environments.

This manuscript presents an exhaustive review of blockchain-based mixing services, aiming to fill the existing gap between academic innovations and real-world implementations. Starting with an identification of the core functionalities and techniques employed by mixing services, the paper delves into detailed explanations of these operational mechanisms. It further outlines an evaluation framework tailored for a rigorous assessment, highlighting the key vulnerabilities and strengths of various solutions. In addition, the study identifies potential attack vectors that compromise these services. The paper explores the dual nature of mixing services: while they contribute to the preservation of privacy—a cornerstone of blockchain technologies—they can also facilitate illicit activities. By addressing key research questions, this study not only offers a comprehensive overview of the current state of mixing services but also sets the stage for future academic discourse in this evolving field.

The rapid development of educational informatisation has made smart classrooms a key platform for advancing innovative teaching methods. However, traditional evaluation systems face challenges related to data security, fairness, and result traceability. This paper introduces a blockchain-based teaching evaluation system for smart classrooms to address these issues and improve teaching quality and management efficiency. The system employs a cloud-network-edge-device architecture, integrating cloud computing, network communication, and edge devices for real-time data collection, secure transmission, and intuitive visualisation. Blockchain technology ensures data integrity and transparency, while the practical Byzantine fault tolerance consensus algorithm maintains system reliability and prevents data manipulation. Experiments conducted at Dalian Jiaotong University demonstrate that the smart classroom improves teaching quality by 20% compared to traditional classrooms. The system is particularly effective in enhancing teaching resources and real-time communication, though improvements in student engagement are still needed. System performance tests indicate that the platform maintains low response times and stability under varying levels of concurrent requests, demonstrating its capability to support high-demand teaching scenarios and ensure data consistency and transparency.

The dramatic fluctuations of cryptocurrency prices in recent years have led to various studies on price forecasting, among which transformer-based forecasting methods have better prediction results on time series data. However, a large number of studies in the past have shown that transformer has the disadvantages of high computational cost and inability to predict global information and does not consider external information. To solve these problems, we propose an adaptive decomposition method based on transformer, which combines transformer with seasonal trends and external factors, i.e. the global information of the time series obtained by the decomposition method through adaptation is combined with external information. We conducted experiments on different datasets, and the results show that our method can significantly improve the performance of the baseline model.

Regulatory frameworks play a large part in the effectiveness of blockchain adoption. This study focused on determining the regulatory policies that promote the growth of blockchain adoption and analysing the impact of these regulatory approaches on blockchain adoption in finance sectors in emerging markets. The study used a comparative policy analysis to compare policies and regulations from chosen emerging markets. The countries included in the analysis are China, India, Kenya, Nigeria, Albania and Turkey. The findings suggest a correlation between supportive policy environments, such as those in Turkey, Albania and India, and more advanced applications of blockchain technology; however, this relationship is likely influenced by additional factors, including economic capacity, institutional readiness and technological infrastructure. In contrast, countries with restrictive policies, such as Kenya and Nigeria, ranked lower in blockchain adoption in the finance sector. Accordingly, a unified approach to blockchain regulations and cooperation between participating stakeholders is recommended. This study was limited in that only a few countries were chosen for analysis, which could have limited the scope of the comparative analysis.

This article presents algorithms and smart contract designs to facilitate recurring bill payments on decentralised platforms using the Ethereum-like Virtual Machine (EVM). The proposed approach enables the inclusion of recurring and deferred transactions, ensuring compatibility with ERC-20 and ERC-777 standards, thereby contributing to the establishment of a novel token standard. The research addresses the growing need for non-custodial mechanisms to support automatic periodic payments in decentralised environments, offering potential benefits to both service providers and customers in various industries.

Smart contracts are the cornerstone of decentralized applications and financial protocols, extending digital currency transactions but also introducing serious security challenges that cause substantial economic losses. Existing solutions primarily target code-level vulnerabilities, which constitute only a portion of all security incidents. Though prior research conducts static analysis across contract lifecycles, they lack the characteristic analysis of vulnerabilities in each stage and the distinction between the vulnerabilities. This paper presents the first empirical study on the security of smart contracts throughout their lifecycle, including deployment and execution, upgrade, and destruction stages. We propose two feature categories: transaction features (contract lifespan and four dynamic features: transaction numbers, transaction amounts, neighbour numbers, and the number of old and new neighbours) and ego network properties (temporal network density and local clustering coefficient). These seven features capture behavioural patterns across dimensions, including activity duration, transaction frequency, financial liquidity, interaction breadth, dynamic interaction changes, and structural properties of the transaction network. Finally, five machine learning models—logistic regression, random forest, support vector machine, decision tree, and K-nearest neighbours—are used to identify vulnerabilities at different stages. Results show that vulnerable contracts exhibit distinct transaction features and ego network properties at different lifecycle stages.