Blockchain technology is one of the most novel technologies that received attention from academia and practitioners in various industries because of its profound nature and the opportunities that it offers, especially in the digital age. Ethereum, which is the largest decentralized blockchain software, was introduced in 2015 and is best known for its smart contracts that facilitate different utilizations over its blockchain network. Also, it can be used as a cryptocurrency, and historically it has been the 2nd most valuable cryptocurrency following Bitcoin. In order to be able to use Ethereum smart contracts over the blockchain network, a transaction fee or a gas fee is set by the network according to that specific task. This study is aimed to develop Machine Learning (ML) models to predict the Ethereum gas fee by using a comparison of Long Short-Term Memory (LSTM) and the Facebook Prophet Model (FPM). The gas fee prediction modeling was done based on the daily dataset of eight years from 2015 to 2022. The results in this study showed that the FPM model in the scenario of this study resulted in a Mean Absolute Error (MAE) of 0.02 and a Root Mean Squared Error (RMSE) of 0.05. However, MAE and RMSE values of the LSTM model turned out equal to 0.006 which showed a higher performance and more accuracy compared to the FPM model.
Jishu Wang, Yaowei Wang, Xuan Zhang, Zhi Jin · 8 authors
Blockchain is a trans-generational technology that is gradually introduced and applied in many fields because of its characteristics such as tamper-proof, traceability, and decentralization. However, the performance bottlenecks of blockchain have been one factor that hinders its practical application. This paper proposes a blockchain performance optimization framework (called LearningChain). We use a temporal convolution network to predict the transaction arrival rate of the blockchain and propose an ensemble learning-based method and a meta-learning-based method to train a blockchain performance prediction model, respectively. We design a performance scoring mechanism to dynamically tune the configuration parameters of the blockchain to optimize the blockchain performance. In addition, we collect and contribute a blockchain performance dataset (called HFBTP) for other researchers to research. The sufficient experimental results and analysis show that LearningChain can effectively optimize blockchain performance. The quantitative and qualitative comparisons with related work demonstrate the superiority and innovation of our work, LearningChain reaches state-of-the-art, is highly applicable, scalable, and can be applied to many practical blockchain-based application scenarios and different blockchain platforms. LearningChain can be complemented with other existing blockchain performance optimization tools and methods to further enhance the effectiveness of blockchain performance optimization.
Manjula K. Pawar, Harshitha S Hiregowdar, Sukruti Joshi
This research paper presents the design and implementation of a land registry system using multiple nodes with Geth, a command-line interface for running Ethereum nodes. The system is designed to address the issues of security and efficiency in the current land registration system. The system uses a distributed database with multiple nodes to store land records, providing redundancy and fault tolerance. The use of a distributed database also ensures that no single point of failure exists, improving the system's reliability. To ensure that the land records are impenetrable and cannot be changed without permission, the system makes use of the Ethereum blockchain. A consensus mechanism is used to implement the blockchain, ensuring that every node on the network concurs with the database's current state. The use of a consensus algorithm ensures that the system is secure and resilient to attacks. To automate the transfer of land ownership, the system also includes a smart contract framework. The smart contract system ensures that all parties involved in a land transaction agree on the terms of the transaction before it is executed. This reduces the possibility of land transaction conflicts. The system was developed using Geth, a commandline interface for managing Ethereum nodes. The system was tested using a simulated network of nodes and was found to be reliable and efficient. The system showed high throughput and low latency, which qualified it for usage in a production setting.
Kulaea Taueveeve Pauu, Jun Wu, Yixin Fan, Qianqian Pan · 5 authors
Natural disasters such as earthquakes can cause damage to critical infrastructures and limit access to vital information, making it difficult for disaster response teams to respond effectively. Unmanned aerial vehicles (UAVs) have the potential to aid and provide real-time information for disaster response teams, however, the need to process distributed learning for huge amounts of interconnected nodes in a graph network poses several challenges. First, distributed learning in graph networks for UAVs is still an open issue, making it difficult to train and share models on such networks. Second, such a network can leak privacy-sensitive information, making it harder to ensure data security. To address these challenges, we propose, in this paper, a novel privacy and blockchain-empowered UAVs-enabled decentralized graph federated learning (DPBE-DGFL) framework for disaster response. The framework includes three phases: (i) local model training utilizing stochastic gradient descent with differential privacy, (ii) model weights integrity authentication using blockchain to ensure secure and efficient sharing of model weights, and (iii) final validator selection and model weights aggregation using a dedicated proof-of-stake, (DPoS), consensus mechanism to ensure efficient and decentralized consensus while maintaining security and integrity. Our DPBE-DGFL framework was evaluated using extensive simulations on EMNIST and real-world disaster datasets from Tonga. The results show that it offers a promising solution for privacy-preserving federated learning in graph networks, balancing privacy protection and model accuracy while maintaining latency, communication, and computational efficiency.
Venkatesan Muthukumar, R. Sivakami, Vinoth Kumar Venkatesan, J. Balajee · 7 authors
The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device’s information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (>70% SDI, >97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.
Mohammad Alja’afreh, Sahel Alouneh, Muath Obaidat, Ali Karime · 5 authors
As technology has advanced, people's lives have been transformed by the virtual world, which has been created by technologies such as the Internet, computers, artificial intelligence, and hardware. The metaverse, a new social ecology connecting the physical and virtual worlds, is rapidly expanding as the demand for virtual reality grows. Privacy, security, high synchronization, and low latency have all been challenged as data volumes and value have grown and the metaverse continues to evolve. As blockchain and intelligent networking technologies continue to evolve, these challenges can be addressed and the metaverse's needs for trusted construction, continuous data interaction, and computing can be satisfied. In order to provide immersive experiences in the metaverse, a comprehensive review of blockchain's role and benefits is essential. The purpose of this survey is to discuss the metaverse's development trend, architecture, and characteristics. This review paper is novel in providing an overview of existing blockchain research, including overviews, applications, and challenges. Furthermore, we summarized the metaverse's applications, emphasizing their significance and areas of development. Using the survey, we are able to discuss open issues, challenges, and future research directions.
Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance, transportation, healthcare, education, and supply chain management. Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges. However, the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes. There is the biggest challenge of data integrity and scalability, including significant computing complexity and inapplicable latency on regional network diversity, operating system diversity, bandwidth diversity, node diversity, etc., for decision-making of data transactions across blockchain-based heterogeneous networks. Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems. To address these issues, today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain. The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network. This paper proposes a full-fledged taxonomy to identify the main obstacles, research gaps, future research directions, effective solutions, and most relevant blockchain-enabled cybersecurity systems. In addition, Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper to meet the goal of maintaining optimal performance data transactions among organizations. Overall, this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.
Ahmad K. Al Hwaitat, Mohammed Amin Almaiah, Aitizaz Ali, Shaha Al‐Otaibi · 7 authors
Most current research on decentralized IoT applications focuses on a specific vulnerability. However, for IoT applications, only a limited number of techniques are dedicated to handling privacy and trust concerns. To address that, blockchain-based solutions that improve the quality of IoT networks are becoming increasingly used. In the context of IoT security, a blockchain-based authentication framework could be used to store and verify the identities of devices in a decentralized manner, allowing them to communicate with each other and with external systems in a secure and trust-less manner. The main issues in the existing blockchain-based IoT system are the complexity and storage overhead. To solve these research issues, we have proposed a unique approach for a massive IoT system based on a permissions-based blockchain that provides data storage optimization and a lightweight authentication mechanism to the users. The proposed method can provide a solution to most of the applications which rely on blockchain technology, especially in assisting with scalability and optimized storage. Additionally, for the first time, we have integrated homomorphic encryption to encrypt the IoT data at the user’s end and upload it to the cloud. The proposed method is compared with other benchmark frameworks based on extensive simulation results. Our research contributes by designing a novel IoT approach based on a trust-aware security approach that increases security and privacy while connecting outstanding IoT services.
Gehui Li, Jing Yang, Tao Yu, Fuquan Yang · 6 authors
In China, the promulgation of a green power identification system has gradually shifted from electricity power generation to consumption. However, there is no mature digital identification system for green power consumption enterprises. Exploiting the open, transparent, and immutable characteristics of blockchains, this study establishes a digital identification system for green power consumption based on blockchain technology. This system uses expert scoring and the entropy weight method as the evaluation algorithm for the green power consumption chain, issues non-fungible tokens as digital identification for consumption enterprises, and realizes the automatic generation of identification through smart contracts. These functions guarantee the credibility of the certification process and the uniqueness of the generated identification. The results of the experiments show that, in the case of multi-user concurrent requests, the number of concurrent users that the system could handle at optimal processing efficiency was 500, and block generation was stable. The proposed system has high practicability and stability.
With the widespread adoption of blockchain technology in various sectors, the problem of difficult value circulation and data transfer between blockchains is becoming more prominent, however, existing cross-chain methods have limited application scenarios and insufficient consideration for cross-chain data protection. To resolve these issues, this paper proposes a secure cross-chain mechanism based on relay chain and smart contract encryption scheme. The proxy nodes from each blockchain are elected to build a new relay chain as a data cross-chain medium. In order to ensure the honesty of proxy nodes, a proxy node selection method that integrates the VRF algorithm is designed. To address the issue of mistrust between blockchains and ensure the secure transmission of cross-chain data, a smart contract based encryption scheme is designed to make other blockchains and relay chains constrain each other. Finally, we evaluated the new mechanism in Ethereum, and experiments showed that the new mechanism is faster than the Wanchain project in processing cross-chain transactions and has stable performance.
A smart contract is a special set of protocols based on blockchain technology to implement the terms oragreements between the parties in the contract. Lots of smart contracts are built and deployed everyday. However, to ensure the safety of smart contracts is still a big challenge. Smart contracts are built to carry out transactions directly related to cryptocurrencies, therefore, the loss of security of smart contracts leads to huge financial losses. Common methods being used to check and to verify smart contract security are heavily dependent on hard rules defined by experts, leading to low detection accuracy and non-scalable, which can be bypassed by experienced attackers. In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract. Then we extract graph features from normalized graphs, and combine graph features with defined security patterns to create combined features. Finally, we implemented normalization to gray scale image and feed it to the Convolutional Neural Network (CNN) to learn for vulnerability detection. Results show significantly improved accuracy compared to previous methods or other models. Specifically, 96,42%, 90,12%, 79% for reentrancy, timestamp dependence and infinite loop
The education management model refers to the system and processes that colleges and universities use to manage and oversee their academic programs and operations. However, with the advent of digital technologies, there has been a growing trend towards the Internet+ college education management model, which integrates digital technologies into all aspects of college education management. This model includes the use of online learning platforms and tools, such as learning management systems (LMS), to deliver courses and manage student progress. It also includes the use of digital technologies for administrative tasks such as admissions, enrolment, and financial aid. However, the educational management model is subjected to the challenge of security for educational data management. Hence, this paper constructed a secure framework model of the Ethereum SDN Cloud Architecture (ESDNarc). The ESDNarc model uses the Software-defined Network (SDN) for the decentralized management of the network, secure transactions, and improved efficiency. The ESDNarch model incorporates the SDN with the cryptography scheme the secure the data. The constructed model uses the double-hashing Elliptical Curve Cryptography (DHECC) for the data stored in the Ethereum blockchain. The performance of the constructed model is evaluated with the KDD data set. Simulation analysis stated that ESDNarch significantly increases the data security in the cloud model for the attacks in the network.
In order to study the standard security access authentication mechanism of intelligent sensing terminals of massive power Internet of Things, In order to study the standard secure access authentication mechanism of intelligent sensing terminal of massive power Internet of Things, a new privacy protection method widely used in block chain is proposed to prove identity. The traditional power IoT cloud-side interaction security access MQTT protocol still has a lot of room for adaptation and optimization. First, the proposed non-interactive zero-knowledge proof identity authentication method reduces the time of traditional standard secure access authentication process; Second, it reduced the computing resources consumed in a large number of intelligent sensors access authentication. The comparison results show that, the access authentication time of this method is 30% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="\sim" display="inline" overflow="scroll"> <mml:mo>∼</mml:mo> </mml:math> 50% less than that of the traditional secure access authentication process. The computing resources consumed during authentication are reduced by 20% to 30% compared with traditional security and secrecy mechanisms.
S. Markkandan, Prem Kumar, R Prathipa, K. Vengatesan · 5 authors
6G networks are predicted to provide new prospects for Smart Cities and Internet of Things (IoT) applications because of their global seamless coverage.Therefore, to fulfil the growing need for huge data rates for 6G and greater applications, network capacity must be enhanced.As a result, there is an increase in spectrum demand.Only by successfully sharing existing spectrum and avoiding spectrum underutilization will the increased demand for cellular services be addressed.As a result, for 6G to achieve considerably enhanced network capacity, efficient spectrum management systems must be developed.As a result, maintaining 6G's predicted huge network capacity in such as heterogeneous environment necessitates the shared exploitation of available spectrum resources through dynamic coordination across device and network, which can be accomplished by incorporating SDN into 6G networks.Due to the increased speeds and reliability of 6G networks, users may have to pay more for energy, to overcome these issues, In this paper, a novel proposed a 6G HetNet spectrum management system based on HSA and Smart Contracts.HSA harmonizes network operation by spreading local decision-making and network-wide policy-making processes between BS and the SDN controller, correspondingly, to relieve any possible controller scalability and latency difficulties.And, to tackle the intricacies of service-level agreements, leverage blockchain's smart contract technology, which allows for automation and trustworthy, transparent radio spectrum negotiation among several parties.This proposed solution is dependable, scalable, and implementable, as seen by the results.
Venkatagurunatham Naidu Kollu, Vijayaraj Janarthanan, Muthulakshmi Karupusamy, R. Manikandan
Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is “FinTech”. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.
Ajmeera Kiran, Prasad Mathivanan, Miroslav Mahdal, К. В. С. С. С. С. Сайрам · 6 authors
The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.
The currently researched trusted data storage encryption methods for metering assets store too little data and have long encryption time delays. In order to solve the above problems, the trusted data storage encryption method for metering assets based on blockchain technology is proposed. blockchain technology can carry out the underlying network data support for the trusted data storage encryption of metering assets, and its data validation technology can be used for metering asset data block processing, and the data analysis is used to achieve storage encryption by comparing and validating the asset measurement results in the Ethernet virtual contract through each node within the blockchain node zone. This paper mainly applies blockchain technology in data storage management system, briefly analyzes the underlying logic of blockchain technology and its application in data storage management system, the core of the system is the storage of information and data, with features such as "unforgeable", "traceable", "open and transparent" and "collective maintenance". The core of the system is the storage of information and data, with features such as "unforgeable", "traceable", "open and transparent", "collective maintenance", etc. Finally, we show the application results of the technology in the system.
Radha Raman Chandan, Awatef Salem Balobaid, Naga Lakshmi Sowjanya Cherukupalli, H L Gururaj · 6 authors
Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the method’s efficacy, and it is compared with more traditional methods.
As the most popular cryptocurrency now, Bitcoin's transaction data is easy to obtain, so de-anonymizing Bitcoin becomes possible. This paper constructs a data set of Bitcoin addresses including 5 categories, analyzes and extracts the transaction features of Bitcoin addresses in more detail based on related work, and proposes two new features of fourth-order transaction moments and sample distribution. New features improve the performance of Bitcoin address classification. The accuracy of the LightGBM model was 0.94 and the F1 score was 0.91. This method can identify unknown types of Bitcoin addresses, which improves the ability of relevant agencies to investigate Bitcoin illegal activities.
Smart contracts are one of the three major characteristics of blockchain, and they are also areas where blockchain has application value and flexibility. In essence, a smart contract is a piece of code implemented in a specific scripting language, which inevitably has the risk of security vulnerabilities. How to accurately and timely detect the vulnerabilities of various smart contracts has become the focus and hot spot of blockchain security research. To detect vulnerabilities in smart contracts, researchers have proposed various analysis methods, including symbolic execution, formal verification and fuzzing. With the rapid development of artificial intelligence technology, more and more deep learning-based methods have been proposed and have achieved good results in several research areas. At present, deep learning-based smart contract vulnerability detection methods have not been investigated and analyzed in detail. This paper first briefly introduces the concept of smart contracts and security events related to smart contract vulnerabilities, then introduces the commonly used smart contract features in deep learning-based methods, and describes the deep learning models commonly used in smart contract vulnerability detection. In addition, in order to further promote the research of deep learning-based smart contract vulnerability detection methods, the recent deep learning-based smart contract vulnerability detection methods are summarized and classified according to their feature extraction forms, and are analyzed and introduced from three perspectives: text processing, static analysis and image processing. Finally, the challenges and future research directions in this field are summarized.