This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.
Blockchain technology has revolutionized various industries by offering transparency, security, and decentralization. The critical aspect of blockchain technology is the consensus protocol, which plays a pivotal role in ensuring the integrity and reliability of distributed ledger systems. The selection of an appropriate consensus protocol for a given blockchain application is a complex and multifaceted decision-making process, influenced by various technical, environmental, and operational factors. This paper presents an integrated multicriteria decision-making (MCDM) approach to facilitate the selection of an optimal blockchain consensus protocol. Through a comprehensive evaluation of criteria, including performance, sustainability, incentives, security, and decentralization, our approach provides a robust decision-making framework for consensus protocol assessment. The results prioritize the importance of performance and security factors in blockchain consensus protocol evaluation. The sensitivity analysis is performed to determine the impact of experts’ weight coefficients on the result. The results prioritize the importance of performance and security in blockchain consensus protocol selection.
This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.
Predictive maintenance in cross-border unmanned logistics systems (CBULS) faces persistent challenges, including data privacy, system heterogeneity, and collaborative efficiency. Existing studies that combine federated learning with blockchain address only partial aspects—such as communication or trust—but fail to effectively handle non-independent and identically distributed (non-IID) data, integrate multi-layer privacy, or design consensus mechanisms tailored to cross-border logistics. This paper proposes a predictive maintenance framework that integrates an improved FedProx algorithm with a hybrid Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) consensus. The framework incorporates zero-knowledge proofs, fully homomorphic encryption, and local differential privacy, while employing hierarchical architecture and sharding for scalability. Simulation results show that the proposed method improves prediction accuracy by 6.9% compared with FedAvg and 3.7% compared with FedProx, enhances privacy protection by over 12%, increases system throughput by approximately 23%, and reduces transaction confirmation latency by nearly 18%. These results demonstrate that the framework provides a secure, efficient, and scalable solution for predictive maintenance in CBULS.
The Internet of Things (IoT) has not only significantly enhanced the efficiency of power marketing business systems but also introduced substantial security risks, particularly concerning the leakage and misuse of sensitive customer data. The current existence of a variety of data security auditing programs is more or less flawed, unable to comprehensively rule out the risk of data leakage. This paper proposes an IoT-driven blockchain-based fast traceability method for electricity marketing sensitive data using the Provenance Vocabulary Model (ProVOC), identifying power marketing sensitive data from the data flowing through the network, designing a structured storage model for sensitive data based on China’s ProVOC data traceability model standard, and then adopting blockchain technology to build a private Ether, generating a blockchain for data flow, reducing the storage space, and improving the speed of contract generation. This paper proposes a fast traceability method for power marketing sensitive data through three key innovations: a ProVOC-based identification mechanism that dynamically extracts sensitive data from network flows; a standard-aligned storage architecture compliant with China’s ProVOC traceability specifications; and a lightweight blockchain framework built on a privatized Ethereum network, which reduces storage overhead by 62% and accelerates smart contract deployment by 2.3 × compared to conventional approaches.
The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.
The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.
This study aims to explore a method for the de-anonymization of Bitcoin addresses based on Formal Concept Analysis (FCA).Although Bitcoin, as a decentralized cryptocurrency, offers user privacy protection, its anonymity has also been exploited by criminals, leading to an increase in illegal activities such as money laundering and terrorist financing.To address this challenge, we propose a novel deanonymization framework that constructs a formal context using Bitcoin transaction data and generates the corresponding concept lattice.By extracting the attribute weight vectors for each category, our model can effectively classify Bitcoin addresses, thereby identifying potential high-risk addresses.
Open access
Advanced Data and IoT Technologies
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Dynamic Spectrum Sharing can enhance spectrum resource utilization by promoting the dynamic distribution of spectrum resources. However, to effectively implement dynamic spectrum resource allocation, certain mechanisms are needed to incentivize primary users to proactively share their spectrum resources. This paper, based on the ERC404 standard and integrating Non-Fungible Token and Fungible Token technologies, proposes a spectrum securitization model to incentivize spectrum resource sharing and implements it on the Ethereum test net.
P. Chinnasamy, G. Charles Babu, Ramesh Kumar Ayyasamy, S. Amutha · 6 authors
6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end-end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end-end delay, and 94% packet delivery ratio.
The advent of 6G networks promises revolutionary advances in dynamism, intelligence, and decentralization. Realizing the full potential of 6G requires adaptable service level agreements (SLAs) that can optimize performance based on dynamic network conditions. In this paper, we suggested a method based on the Hyperledger Sawtooth blockchain’s smart contract with the Reptile meta-learning algorithm to solve the rigidity of static SLA and centralization problems. In order to sustain the quality of service in the radio access network and core network domain of 6G networks, this work focuses on SLA management for efficient resource allocation for the eMBB-plus slice. Our approach entails breaking down static SLAs into finer-grained components, transferring those components onto Hyperledger Sawtooth smart contracts, and using the Reptile meta-learning algorithm to forecast SLA metrics and resource requirements. A dynamic tariff model, also proposed within the smart contract, handles increased user demands. We evaluate the solution by analyzing Reptile performance, resource allocation, and SLA violations under dynamic demands. Results demonstrate the efficiency of this AI-driven, blockchain-based approach for automated, optimized 6G eMBB-plus resource management adhering to dynamic fine-grained SLAs. This work highlights the synergistic potential of AI and blockchain for trusted and intelligent 6G service delivery.
Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of "data silo". Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.
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 users’ 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 algorithm’s efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
Lawrence Nforh CheSuh, Ramón Ángel Fernández Díaz, Jose Manuel Alija-Perez, Carmen Benavides · 5 authors
The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers.
Fahad F. Alruwaili, Manal Abdullah Alohali, Nouf Aljaffan, Asma A. Alhashmi · 6 authors
The fast development of smart home devices and the Internet of Things (IoTs) presents unprecedented accessibility into our day-to-day lives; however, it has also increased major problems regarding security and privacy. A smart home network is a vital element of modern home automation systems, enabling the interconnectivity and control of different smart devices. These networks allow homeowners to remotely control lighting, security, temperature, and entertainment systems via voice commands or smartphones. These offer energy efficiency, convenience, and improved security by permitting residents to monitor and modify their living surroundings. Safeguarding the flexibility of smart home networks against cyberattacks and unauthorized access is important to comprehending the maximum ability of smart living while retaining data integrity and privacy of connected devices. This research develops the Blockchain with Red-Tailed Hawk Algorithm-Enabled Deep Learning (BC-RTHADL) model, aimed to strengthen the safety of smart home systems. BC-RTHADL integrates the safety features of blockchain with a strong malicious action recognition procedure. The blockchain module certifies immutability, transparency, and decentralization, donating to a safe smart home atmosphere. The malicious action detection influences the Red-Tailed Hawk Algorithm for feature selection and an ensemble of Extreme Learning Machine (ELM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) techniques for precise recognition. The Equilibrium Optimizer algorithm enhances parameters for improved effectiveness. Complete tests show the greater performance of BC-RTHADL across numerous metrics, reaffirming its promising potential in safeguarding smart home networks.
With the development of blockchain technology and the economy, the demand for data interaction and application collaboration between blockchains is increasing. Due to the differences in the data structure, interface protocols, consensus mechanisms, and even business models, new "chain silos" have been formed among blockchain applications, which limit the interoperability of asset exchange and business collaboration among blockchains. Based on the full analysis of blockchain technology characteristics and blockchain Internet development needs, this paper designs a blockchain cross-chain system based on the unified NFT identity identification technology, address tracking technology, and seamless interconnection of mobile security cross-chain technology, based on the "NFT + Cross-chain bridge". Based on the service mode, verification mode, and security of Bridges, six model timeliness, smart contract robustness, and convergence radius were selected from 44 cross-chain Bridges for experimental verification. Finally, it is concluded that the overall performance of the blockchain cross-chain system based on "NFT + Cross-chain bridge" is far more universal and better security than the performance of ordinary Bridges.
In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.
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.
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.
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.