The process of efficiently meeting customer needs through the seamless transfer of goods, services, and information is known as supply chain management. Because of centralized control, trustless networks, and occasionally manual processes, modern supply chain management solutions lack traceability, transparency, security, and decentralization. As blockchain technology is decentralized and immutable, it provides a solution that ensures transactions are transparent and viewable in real time. Due to traditional proof-based algorithms like Proof of Work (PoW) and Proof of Stake (PoS) or other voting-based algorithms, the Blockchain utilized for traditional supply chain management (SCM) has issues with high energy consumption, scalability, and centralization dangers. In this study, we will offer an optimal Blockchain-based model that evaluates nodes based on multiple parameters, including processing power, network latency, uptime, and reputation, using a consensus mechanism based on a weighted leader selection technique. By dynamically assigning weights to these factors, this study can overcome the limitations of single factor leader selection and guarantee validator selection that is efficient, adaptable, fair and sustainable. This study also examines the concept to demonstrate how it might support decentralization in modern supply chain management (SCM) systems while enhancing traceability, security, and transparency through more efficient use of resources.
Asif Ali Laghari, Awais Khan Jumani, Shoulin Yin, Muhammad Bux Alvi · 7 authors
Blockchain is a decentralized, public, and distributed ledger designed to securely record and track transactions. It possesses the potential to transform various industries, including healthcare, supply chain management, and financial services, by enhancing transparency, efficiency, and trust. Despite its promise, blockchain development continues to face several challenges, particularly concerning security, scalability, and standardization. This paper provides a comprehensive analysis of blockchain technology, focusing on its quality of service (QoS), security mechanisms, and the latest frameworks and models shaping its evolution. Furthermore, it examines existing limitations and identifies key open research challenges that must be addressed for broader adoption. The findings suggest that blockchain can substantially improve operational efficiency and data integrity across multiple domains; however, realizing its full potential requires continued research and technological advancement to overcome current barriers.
With a secure distributed ledger system, blockchain technology helps to shape the healthcare sector in terms of transparency and accountability. This research paper highlights the transformational impact that blockchain technology plays in the healthcare sector. It focuses on a variety of technologies, including patient data security, real-time monitoring enabled Internet of Things (IoTs). This paper also addresses the revolutionary impact that current technology, such as cryptography and Zero Knowledge Proofs (ZKPs), has played in shaping modern technology and improving privacy and security in the health care system. This study contrasts existing blockchain technologies with emergent developments that aid in the creation of a decentralized, safe ecosystem that promotes accountability. Blockchain provides a decentralized, immutable, and transparent ledger. It enhances security, privacy, and trust in data transactions without relying on centralized servers.
Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments: The data is vulnerable to tampering and breaches, which would completely undermine the accuracy of AI-driven insights, when running data pipelines in hybrid cloud setups.Blockchain Enhanced AI is a framework that combines the unchangeable records of blockchain technology with AI to check the accuracy of real-time data in multi-cloud systems. This can be done with the help of smart contracts that automatically flag anomalies, using sophisticated machine learning models such as LSTM networks. We can virtually eliminate the possibility of man-in-the-middle attacks by hashing the data blocks and validating them via consensus algorithms. In software engineering, the process streamlines the DevOps pipeline and reduces the risk of breaches by 30% on simulations in AWS and Azure. At the heart of the framework lies a robust core architecture and also includes privacy-preserving zero-knowledge proofs, and we’ve put this system to the test with empirical results in prototypes processing enormous petabyte-scale datasets. Attendees will be able to learn about the real-world implementation of this technology, challenges to scaling, and the ethical concerns around decentralized AI governance, and in doing so will help in developing more secure cloud-native applications.
E. Sravanthi, Pabbathi Laxmiprasanna, Mulukutla Jahnavi, Kancharla Kritika Reddy
The increasing reliance on digital systems in law enforcement has emphasized the need for secure, transparent, and reliable mechanisms to manage crime evidence. In existing systems, evidence management is typically handled through centralized databases and manual record-keeping, where crime reports, officer details, and evidentiary materials are stored in a single controlled environment. This approach introduces critical challenges such as data tampering, unauthorized access, loss of sensitive information, and lack of transparency, which can weaken trust and complicate legal proceedings. Furthermore, storing evidence in physical formats or unsecured digital systems makes it difficult to ensure authenticity and maintain a proper Chain of Custody (CoC). These limitations highlight the necessity for a system that ensures data integrity, traceability, and secure verification. To overcome these issues, the proposed framework adopts a decentralized architecture using Blockchain technology and Smart Contracts to provide immutability, transparency, and enhanced security of evidence records. The system leverages Ethereum for decentralized data storage, Web3 for enabling interaction between the application and the blockchain network, and Django as the web framework for managing the user interface, file handling, and administrative functionalities. Authorized officers can securely upload, access, and manage evidence, while administrators can monitor and verify transactions in real time. Each evidence record is assigned a unique identifier and permanently stored on the blockchain, preventing unauthorized modification and ensuring a verifiable audit trail. Although the system does not utilize Machine Learning (ML) or Deep Learning (DL), it effectively employs smart contracts-based automation for secure evidence tracking, thereby improving accountability, legal reliability, and operational efficiency.
Gaurav Kokane, Dr. Pratibha V. Kashid, Prathamesh Pandit, Hitesh Patil · 5 authors
ABSTRACT Cryptocurrency trading has rapidly evolved into a highly dynamic and technology-driven financial domain, attracting significant attention from investors, researchers, and institutions worldwide. This paper presents a comprehensive review of modern cryptocurrency trading platforms by combining blockchain technology, artificial intelligence, and advanced trading mechanisms to create a secure, efficient, and scalable trading ecosystem. The study highlights the use of deep learning approaches such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and attention-based models for improving cryptocurrency price prediction. These techniques utilize technical indicators, trading patterns, and social media data to enhance prediction accuracy. In addition, reinforcement learning strategies are explored to optimize trading decisions and improve performance under highly volatile market conditions. Furthermore, the paper discusses real-time data integration using APIs, secure authentication mechanisms, and scalable system architectures required for continuous trading operations. It also examines the regulatory landscape of cryptocurrency, particularly in the Indian context, including taxation policies and emerging concepts like Central Bank Digital Currencies (CBDCs). Overall, this review provides insights into the development of intelligent, secure, and user-friendly cryptocurrency trading platforms such as CryptoVista. Keywords: Cryptocurrency, Blockchain, Deep Learning, Reinforcement Learning, Smart Contracts, Real-Time Data, Trading Platforms, Security, Scalability, CBDC
The explosion of multimodal healthcare data such as medical images, physiological signals, and electronic health records has posed major security storage problems, credible data sharing, and correct disease diagnosis. Traditional healthcare is usually vulnerable to privacy concerns, unauthorized access, and poor analytic abilities. To handle the above challenges, this paper suggests STMD-BTNet, an intelligent and secure architecture that combines blockchain-based trust management and deep neural networks to process multimodal healthcare data reliably. The proposed system secures patient data with a dynamic hash-based session key generation system and secures the transmission with a verified blockchain bridge that uses a trust-conscious Proof-of-Stake consensus system. Access control with smart contracts can be used to provide access to sensitive records by authorized medical professionals. A multimodal deep learning model that incorporates convolutional neural networks to process medical images, long short-term memory networks to process physiological signals, and fusion layer to combine features of clinical attributes allows patients to receive the correct diagnosis. Experimental results on publicly accessible healthcare datasets show better performance with accuracy of 98.62, precision of 98.45, recall of 98.30 and AUC of 99.12 in the combined application of the multimodal, and low latency and improved data security. Comparative analysis has proved that STMD-BTNet is more reliable to diagnose, scale, and trust well than current deep learning and blockchain-based methods and is therefore applicable in next-generation intelligent healthcare infrastructures.
The proposed research is to provide a secure framework for transmitting and managing IoT sensor data using blockchain technology. The system involves different types of users, each with the ability to register, log in, and upload IoT sensor data. The data transmission is secured using the SHA-256 cryptographic algorithm, ensuring that sensor data is protected from unauthorized access. Both users can utilize Blockchain APIs or smart contracts to increase security and maintain transparency during data exchange. Additionally, users can decrypt the sensor data provided by each other, ensuring secure and trustworthy communication between devices within the IoT network. This system leverages blockchain decentralized nature to protect the data sensor data, prevent tampering and unauthorized modifications. The system’s performance is tested on different key sectors such as financial management, Health sector, supply chain and E-Governance data.
SOUJANYA SOUJANYA, N. JYOTHI N. JYOTHI, G. KUSHAL G. KUSHAL, M.THRILOCHAN M.THRILOCHAN · 5 authors
The criminal activities in India are increasing at a rapid rate. Many of these activities go unreported. Even after having an online portal for the police for storing FIRs and NCRs, most of the FIRs are handwritten as a traditional practice. In most of the cases, the complainant has to be present in the police station to file a cognizable offense. An effective system for e-governance was started in 2009 named Crime and Criminal Tracking Network and Systems (CCTNS) for the entire country. However, it is a centralized system for a particular state. Thus, there is a need for a completely decentralized system for assuring that there is no central point of failure in the system and complaints are managed securely protected from unauthorized access. Our aim is to propose a blockchain-based solution to manage complaints against both cognizable and non-cognizable offenses. The FIR filed by the police will be encrypted, stored in the IPFS and hash is added to the blockchain network. If the police decide not to file the FIR under pressure or deny receiving any complaint, then the complainant will have strong proof against him/her as the complaint along with its timestamp was stored on the blockchain network. Having all the records stored in an immutable database would remove any chances of the FIR/NCR being tampered and going unnoticed. Keywords – Blockchain Technology, Police Complaint Management System, Smart Contracts, Distributed Ledger Technology (DLT), Decentralized Application (DApp), Data Integrity, Tamper-Proof Records, Immutable Audit Trail, Zero-Knowledge Proof (ZKP), Hyperledger Fabric and Permissioned Blockchain
SHAIK SANA SHAIK SANA, N. SOUJANYA N. SOUJANYA, MOHAMMED MAJEED MOHAMMED MAJEED, BUCHI PAVITHRA BUCHI PAVITHRA · 6 authors
In the current digital era, social media platforms have become pivotal for individuals to express their opinions, political views, and product reviews. However, the centralized nature of traditional social media systems poses significant risks related to data breaches, server crashes, and single points of failure. To address these challenges, this paper proposes a novel approach to migrate from centralized to decentralized social media platforms by leveraging Blockchain technology. Blockchain ensures data immutability, decentralized storage, and enhanced security by distributing data across multiple nodes. Any tampering with data is immediately detectable due to the cryptographic linkage of data blocks through unique SHA-256 hash codes. The proposed system, named dTweets, enables users to post and view tweets securely using smart contracts written in Solidity and deployed on the Ethereum network. This decentralization prevents fraudulent users from spreading misinformation or unauthorized advertisements. Experimental results demonstrate that the proposed system achieves strong data integrity, tamper resistance, and transparent operation while maintaining acceptable transaction latency. This implementation provides a robust foundation for a secure and tamper-proof social media ecosystem. KEYWORDS : Blockchain, Decentralized Social Media, Data Security, Privacy Protection, Smart Contracts, SHA-256, Proof of Work, Distributed Ledger, Ethereum, Solidity, dTweets, Secure Data Storage, Web3.
Forensic evidence management in real-world environments presents numerous challenges such as data tampering, unauthorized access, lack of transparency, and inefficiencies in maintaining the chain of custody. In this project, we propose a robust system for managing forensic evidence using blockchain technology by integrating both traditional database methods and decentralized ledger mechanisms. The proposed system utilizes blockchain features such as cryptographic hashing, distributed storage, and consensus protocols along with smart contracts to securely store, verify, and track forensic evidence throughout its lifecycle [1]. A comprehensive forensic dataset consisting of digital evidence records is used to conduct extensive experiments. The system is evaluated by combining blockchain storage with off-chain databases to efficiently handle large volumes of data while ensuring integrity through hash references stored on the blockchain. Multiple configurations of storage and verification techniques have been tested to identify the most effective approach for secure evidence management. The analysis of results indicates that the hybrid blockchain model integrated with smart contracts provides superior performance in terms of data integrity, transparency, and resistance to tampering [2]. The study also compares traditional centralized systems with blockchain-based approaches, highlighting the advantages of decentralization in handling real-world forensic data. The proposed system significantly improves the reliability and efficiency of evidence tracking even under challenging conditions, making it suitable for applications such as cybercrime investigation, digital forensics, and legal evidence management systems [3]. Keywords – Blockchain, Forensic Evidence Management, Cryptographic Hashing, Smart Contracts, Distributed Ledger, Data Integrity, Chain of Custody, Cybersecurity, Digital Forensics, Decentralization.
Fahd Alhaidari, Sarah AlQahtani, Noura AlDossary, Rachid Zagrouba
As the Internet of Things (IoT) continues to expand across various domains, the number of connected devices is rapidly increasing, exposing IoT environments to large-scale Distributed Denial-of-Service (DDoS) botnet attacks. Due to limited computational and memory resources, IoT devices remain particularly vulnerable to traffic flooding and coordinated malicious behavior. This paper presents a layered security framework that integrates machine learning, protected gateway servers, and blockchain-based smart contracts to detect and mitigate DDoS botnet attacks in IoT environments. The proposed model performs behavioral anomaly detection off-chain using a two-stage machine learning process, while leveraging smart contracts on the blockchain for tamper-resistant logging, automated policy enforcement, and controlled economic penalties. A bounded spending mechanism and quarantine policy are introduced to discourage large-scale malicious traffic while limiting the impact on compromised legitimate devices. The system architecture and enforcement algorithms are presented to demonstrate the feasibility, scalability, and security advantages of the proposed framework.
Abstract - The healthcare supply chain faces challenges such as inefficient procurement, lack of transparency, counterfeit medicines, and poor tracking mechanisms. This paper proposes a blockchain-based solution integrating smart contracts and decentralized storage systems to enhance traceability, security, and efficiency. The system connects stakeholders including manufacturers, distributors, retailers, and healthcare providers through the Ethereum blockchain. Smart contracts automate transactions, while IPFS and Hyperledger Fabric ensure secure and decentralized storage. The proposed framework improves transparency, reduces fraud, and enhances communication across the supply chain. Experimental results demonstrate improved security, cost efficiency, and system reliability. Key Words: Blockchain Technology, Healthcare Supply Chain, Smart Contracts, Ethereum, Decentralized Storage, IPFS (InterPlanetary File System), Hyperledger Fabric, Supply Chain Management, Data Security, Traceability, Transparency, Counterfeit Drug Prevention, Distributed Ledger Technology
This paper argues that India's Goods and Services Tax Network has already produced, through tax incentive rather than cryptographic consensus, the supply chain properties — traceability, transparency, fraud reduction, and audit trail — that the blockchain literature proposes to deliver through distributed ledger technology. The argument is not that blockchain does not work. It is that the mechanism that produces tamper-resistance is the incentive, not the technology, and India already has that mechanism at national scale. The paper's original theoretical contribution is a two-player simultaneous-move game formalizing the bilateral incentive structure that the VAT self-enforcement literature has assumed in prose but never derived from primitives. The unique Nash equilibrium (F,D) — formal supplier, demanding buyer — is obtained by iterated elimination of weakly dominated strategies and sustained by a single precise condition: τv > c_B, the input tax credit exceeds the buyer's cost of sourcing from a registered alternative. No audit is required at the transaction level. The ITC does the work that enforcement cannot. The upstream formalization cascade — empirically documented by Patnaik (2026) as a doubling of effects over five years — follows directly as this equilibrium applied iteratively upstream, tier by tier, without government intervention at each stage. To the author's knowledge, this micro-foundation does not appear elsewhere in the VAT literature. Pomeranz (2015), Kleven et al. (2011), and de Paula and Scheinkman (2010) treat the self-enforcement intuition as motivation or derive aggregate implications; none writes down the strategic form game or states the equilibrium condition in falsifiable form. The empirical case rests on scale. FY2024-25 gross collections of Rs. 22.08 lakh crore (approx. USD 263 billion). April 2025 single-month record of Rs. 2.37 lakh crore (approx. USD 28 billion). 1.51 crore active registered taxpayers. Six phases of e-invoicing threshold reduction from Rs. 500 crore to Rs. 5 crore, directionally toward universal pre-validated coverage. GST 2.0 implemented September 22, 2025. The Production Linked Incentive scheme disbursing billions to Apple's contract manufacturers on the basis of GSTN-verified production data — the sovereign proof that the infrastructure is trusted for the highest-stakes commercial verification the government performs. The implication for Indian FMCG, pharmaceutical, and logistics firms is direct: private blockchain consortia built to solve domestic supply chain transparency problems are solving a solved problem at non-zero cost.
G. Naveen Kumar, B. Vaishnavi, Ch. Gayathri Bharghavi, K. Shveni
The Smart Contract Farming System is a digitalized platform created to connect farmers and buyers more reliably and transparently. Its main goal is to reduce the gap between both buyers and farmers by using secure digitized agreements that clearly define terms and conditions, which helps to build trust and ensures that transactions are fair and well-structured. The platform is developed using Vite and React, through which users can easily register, explore crop listings, view contracts, and interact in real time. On the backend, Node.js and Express.js handle the core application logic, including API services, authentication, contract processing, and transaction management. All data is securely stored and managed using MongoDB, ensuring consistency and reliability. One of the key features of the system is the Price Prediction Module, which works before a contract is finalized. This module uses agricultural datasets collected from IEEE research publications. With the help of Python-based machine learning models, the system predicts crop prices by analyzing historical data, seasonal trends, and market needs and supply. It helps farmers and buyers in making informed decisions and agreeing on fair prices. Once the price is decided, digital contracts are created and accepted by both parties. This system also includes crops based on agricultural seasons such as Kharif, Rabi, and Zaid, which helps in better planning and avoids mismatches between supply and demand. In addition to contract management, the platform involves features like dispute resolution tools and analytical dashboards, which make the overall process more efficient and transparent. Payments are secured through trusted methods such as UPI and escrow systems, ensuring safe and reliable transactions. The feature that makes this application more effective is the Crop Insurance module, which allows farmers to enroll in government-supported insurance schemes. This protects them from unexpected risks like floods, droughts, or pest attacks. Overall, the system is designed to be scalable, efficient, and user-friendly. It strengthens farmers by giving them assured market access while helping buyers get a consistent and trustworthy supply of crops.
Blockchain technology has emerged as one of the most disruptive forces in modern computing, fundamentally reshaping how digital ecosystems manage trust, transparency, and decentralization. This paper presents a comprehensive analysis of blockchain architecture—covering distributed ledger mechanisms, consensus protocols, and smart contract frameworks—and examines their transformative impact across domains including finance, healthcare, supply chain, and governance. A secondary theme of the paper explores the growing role of Artificial Intelligence (AI) as a convergent and supporting technology to blockchain, particularly in areas of anomaly detection, intelligent contract automation, and predictive analytics. Using a structured literature review methodology, we identify key architectural components, survey real-world applications, evaluate current limitations, and outline future research directions. Findings suggest that while blockchain independently offers significant systemic advantages, its integration with AI amplifies scalability, security, and decision-making capabilities—heralding a new paradigm for digital infrastructure.
Prof. Ambreen Anees, Alisha Abbasi Shaikh, Aman Ullah Khan, Mohammad Fahad Kirmani · 5 authors
The rapid digital transformation of healthcare systems has significantly improved the storage, accessibility, and management of patient information; however, it has also introduced serious challenges related to data security, privacy, and trust. Traditional centralized medical record systems are vulnerable to single points of failure, unauthorized access, and data breaches, which may compromise sensitive patient data. This paper proposes a decentralized framework for secure medical records management using blockchain technology. The system utilizes a distributed ledger to store medical data in a tamper-resistant and immutable manner, ensuring integrity and transparency. Cryptographic techniques are employed to encrypt patient data and enforce secure access control, allowing only authorized users to retrieve or update records. Additionally, smart contracts are used to automate access permissions and eliminate the need for intermediaries, improving efficiency. By removing dependence on a central authority, the proposed approach enhances reliability, security, and trust among stakeholders while ensuring privacy protection and controlled data sharing in modern healthcare environments.
Alisha Abbasi Shaikh, Aman Ullah Khan, Mohammad Fahad Kirmani, S. Ali
The rapid digital transformation of healthcare systems has significantly improved the storage, accessibility, and management of patient information; however, it has also introduced serious challenges related to data security, privacy, and trust. Traditional centralized medical record systems are vulnerable to single points of failure, unauthorized access, and data breaches, which may compromise sensitive patient data. This paper proposes a decentralized framework for secure medical records management using blockchain technology. The system utilizes a distributed ledger to store medical data in a tamper-resistant and immutable manner, ensuring integrity and transparency. Cryptographic techniques are employed to encrypt patient data and enforce secure access control, allowing only authorized users to retrieve or update records. Additionally, smart contracts are used to automate access permissions and eliminate the need for intermediaries, improving efficiency. By removing dependence on a central authority, the proposed approach enhances reliability, security, and trust among stakeholders while ensuring privacy protection and controlled data sharing in modern healthcare environments.
Over the past years, there has been increased risk of forging and replicating academic credentials unauthorized, and manipulation of data due to fast computerization of academic credentials. The traditional verification system that is centred on the Public Key Infrastructure (PKI), has included instances such as centralized control, the lack of transparency, and vulnerability to points of failures. Such challenges are suggesting a decentralized approach to the generation of digital certificates as well as their validation with the assistance of a blockchain Technology that is secure in nature. The suggested system will use cryptographic hashing, smart contracts using Ethereum and distributed ledger mechanisms to provide integrity, authenticity, and immutability of data. The blockchain has certificates in the hash values that can be easily verified and without the involvement of middle men. The framework will also enhance trust among the stakeholders as they will be in a position to ensure validation without disruption. As it is revealed through the experiment analysis and modular evaluation, the offered solution enhances the effectiveness of the verification towards its significant extent, the chance of fraud decrease, and offers a solution which can be further scaled and become suitable in the contemporary digital certification systems.
Dr.B.Swathi Dr.B.Swathi, SAANIYA ARSHI, ARABOTHU ANVESH, MOHAMMED AYAAN AHMED · 5 authors
The quick adoption of blockchain technology and generative AI is a major factor in the world's electricity use, which raises concerns about their long-term environmental impact. To save energy, the first thing you need to do is figure out how much energy you are already using. But because blockchain and generative AI are both cloud-based services, it's not easy to understand how much energy they use when they're not at your site. This makes it harder for companies and organisations that want to improve the accuracy of calculating Scope 3 emissions. This study determines the energy consumption of these technologies at both the system level and per-use basis, comparing them to traditional services such as payment networks and web search engines. For instance, Bitcoin, which uses a Proof of Work (PoW) blockchain, uses about 121 TWh, or 0.43% of all the electricity used in the world. It also uses 720,000 times more energy per transaction than the Visa payment system. When Ethereum switched to Proof of Stake (PoS) in 2022, it used 99.988% less energy, showing how much more efficient things can be.Generative AI models also use a lot of energy, especially when they are being trained and used to make predictions. For instance, it took about 9,450 MWh of energy to train GPT-4, and it took more than 500 MWh of energy to do inference work every day. Inference, which is always powered by user activity, is often more resource-intensive than the training process. The authors say that we need to learn more about and lessen the environmental effects of these technologies right away. Possible solutions include energy-efficient consensus mechanisms or giving AIs the ability to better optimise their own lifecycle. The report is meant to help businesses think about how to use technology in a way that is good for the environment as part of a better or more complete Scope 3 emissions strategy.
Financial Fraud has become increasingly common today due to the decentralized finance systems. It involves illegal activities that take over our finances without our knowledge, potentially causing huge losses and negatively affecting economic integrity. Financial fraud erodes trust among the general public, investors, and customers, destabilizing the financial system and hindering economic development. In this Research paper, we aim to explore methods for preventing these fraudulent activities using Artificial Intelligence. It studies the methods and tools we can use to reduce financial fraud. As technology advances, we now have artificial intelligence, which enables us to use modern techniques to combat fraud. We can use various Artificial Intelligence tools like Machine Learning, Deep Learning, Natural Language Processing, Anomaly Detection, Reinforcement Learning, Graph method, and various other tools to recognize the unidentified patterns in our financial transactions and save ourselves from financial fraud. Furthermore, it is essential to implement robust security systems within decentralized finance platforms. This study on enhancing security systems and preventing financial fraud will be helpful to future developers, Researchers, Investors, Individuals, Regulatory bodies, and Security Firms. The goal is to make decentralized finance systems more secure to mitigate the risk of financial fraud and to protect the economic integrity for sustained economic development. Based on this study we will able to upgrade the security system of our financial transactions by using various artificial intelligence tools and can reduce the number of frauds. While completely eliminating financial fraud is challenging, we can significantly reduce it through concerted efforts, creating awareness, utilizing artificial intelligence tools, and exercising vigilance.
Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.