Railway Cargo Systems (RCS) are often associated with issues such as data security, lack of transparency, and inefficiencies in terms of storage requirements and overall operability.This work proposes a novel technique combining blockchain technology with cloud computing to create a secure and streamlined railway cargo system.By leveraging the power of blockchain's distributed ledger and immutability, the system guarantees data integrity and fosters trust among all participants.Cloud computing, on the other hand, injects scalability, real-time data processing, and accessibility for every stakeholder involved in the network.The proposed integration promises significant improvements across various aspects of railway cargo operations.Firstly, enhanced security is achieved by storing transactions and cargo information permanently on the blockchain, significantly reducing the risk of fraud and unauthorized data alterations.Secondly, increased transparency is realized through a shared ledger accessible to all participants, enabling real-time tracking and clear visibility of cargo movement throughout the journey.Thirdly, streamlined processes through automated document handling and by implementing smart contracts on the blockchain lead to improved efficiency.Elimination of paper-based documentation and various intermediary parties achieves the desired cost savings.Finally, the potential benefits and challenges associated with the implementation of our proposed system in the railway cargo industry are assessed, which include the scalability limitations and seamless interoperability between different blockchain platforms.
Blockchain technology has emerged as one of the most transformative innovations of the 21st century, fundamentally reshaping how digital transactions are recorded, verified, and secured across distributed networks without centralized intermediaries. Originally conceived by Satoshi Nakamoto in 2008 as the underlying architecture for Bitcoin, blockchain has evolved far beyond cryptocurrency applications to encompass smart contracts, decentralized finance, supply chain management, healthcare systems, and enterprise solutions. This comprehensive review provides an accessible yet thorough examination of blockchain technology, targeting readers from beginner to intermediate levels seeking to understand both theoretical foundations and practical implementations. We systematically explore the foundational principles of blockchain architecture, including distributed ledger technology, block structure and chain formation, Merkle tree organization, and peer-to-peer network topologies. The paper provides in-depth analysis of cryptographic primitives including hash functions, public-key cryptography, elliptic curve digital signatures, and emerging quantum-resistant approaches. We examine diverse consensus mechanisms ranging from proof-of-work to proof-of-stake variants, Byzantine fault tolerance protocols, and hybrid approaches, analyzing their trade-offs in security, decentralization, and performance. The review extensively covers smart contract platforms with emphasis on Ethereum's architecture, vulnerability patterns, and security best practices. Critical scalability challenges are addressed through examination of layer-two solutions including Lightning Network, state channels, rollups, and sharding protocols. We analyze security threats across network, consensus, and application layers, alongside privacy-enhancing technologies such as zeroknowledge proofs and confidential transactions. Real-world applications are explored across financial services, supply chain management, healthcare, Internet of Things, and digital identity systems. The paper examines enterprise blockchain frameworks, particularly Hyperledger Fabric's permissioned architecture, comparing public and private blockchain tradeoffs. Finally, we discuss current challenges including energy consumption, regulatory uncertainty, and interoperability limitations, while exploring future research directions in quantum resistance and cross-chain protocols. By synthesizing insights from 75 peer-reviewed sources spanning foundational research, recent advances, and practical implementations, this review serves as a comprehensive resource for researchers, practitioners, and students seeking to understand blockchain technology's current state and transformative potential.
Rapid digital transformation across financial, e-commerce, and decentralized platforms has amplified the need for secure, transparent, and resilient transaction systems. Conventional security mechanisms often fail to address sophisticated cyber threats, identity fraud, and evolving attack patterns, highlighting the necessity for integrated technological solutions. The convergence of Blockchain and Artificial Intelligence (AI) establishes a robust framework that combines immutable, decentralized ledger structures with adaptive, intelligent analytics. Blockchain ensures transactional integrity, data provenance, and decentralized trust, while AI facilitates real-time anomaly detection, predictive risk scoring, and automated decision-making. This synergy enhances digital identity management, strengthens access control, and mitigates fraud by enabling continuous monitoring, behavioral analysis, and transparent verification processes. Applications extend to secure payments, smart contracts, cross-border transactions, and decentralized finance ecosystems, demonstrating improved operational efficiency, scalability, and resilience. The chapter also explores privacy-preserving computation, federated learning, and explainable AI frameworks to ensure ethical and accountable deployment of intelligent transaction systems. By integrating structural security with predictive intelligence, Blockchain-aided AI frameworks establish a next-generation foundation for secure digital transactions, fostering trust, regulatory compliance, and systemic reliability across global digital networks.
The shortcomings of centralized authentication have led to a move toward decentralized, tamper-resistant solutions as vehicle networks develop. An organized review of blockchain and cryptographic techniques for vehicle authentication and verification is presented in this study. It divides existing approaches into identity management models (decentralized IDs, PKI-less systems), cryptographic techniques (ECC, zero-knowledge proofs, group signatures), consensus mechanisms (PBFT, PoW, DPoS), and hybrid blockchain-IoT frameworks. The analysis examines trade-offs between security, latency, and scalability while presenting a novel taxonomy that matches focused solutions with risks unique to VANETs, like message forgery and Sybil attacks. The increasing use of privacy-preserving authentication techniques and the possibility of post-quantum secure blockchain systems are highlighted. Important insights for boosting resilience and confidence in next vehicle systems are provided by this work.
Ashwag Alotaibi, Huda Aldawghan, M. M. Hafizur Rahman
This study summarizes the body of research on the IoT and NFTs overlap, highlighting important security concerns, the function of blockchain technology, and implications for future study and smart environment applications. IoT devices provide creative solutions that boost operational effectiveness and enhance user experiences as they spread throughout different sectors. But there are also serious drawbacks to this expansion, especially in terms of security and privacy. At the same time, NFTs unique digital assets verified by blockchain technology—have become extremely popular because of their unique features and wide range of uses. This paper carefully looks at how security frameworks in digital ecosystems may be impacted by the integration of IoT and NFTs. The results emphasize how urgently this integration must be studied further to minimize new risks and maximize the advantages of IoT and NFTs across a variety of sectors. The study intends to contribute to a more secure and effective IoT ecosystem by examining the difficulties presented by this integration. Contributing to the development of a more robust and secure IoT ecosystem is the ultimate aim of this research. This study aims to open the door for future developments that optimize the benefits between the two technologies while reducing risks by recognizing and evaluating the difficulties brought about by the integration of IoT and NFTs. Both academics and industry stakeholders navigating the rapidly changing IoT and blockchain world will find great significance in the results of this research.
In recent years, blockchain technology has gained considerable attention, with increasing interest in diverse fields, including banking, Retail, consumer products, Insurance, Real Estate, Government, healthcare, Supply Chain, and the automotive industry. Blockchain offers a secure, distributed database that can run without a central authority or administrator. Blockchain uses a distributed, peer-to-peer network called Digital Blocks to create a continuous, growing list of ordered records. Each transaction, represented in a cryptographically signed block, is then automatically authenticated by the network. Over time, however, it has become clear that the impact of blockchain as a technology is likely to be much broader than just the cryptocurrency domain and far deeper than simple distributed ledger storage. This detailed survey aims to summarize the most significant developments in blockchain implementation. This article will examine different domains where blockchain has been impacted and where implementation is expected in the future.
In recent years, blockchain technologies such as Ethereum have secured widespread adoption, yet they have also become increasingly targeted by fraudulent activities. Discovering these fraudulent patterns is challenging due to the complexity and size of transaction data. This study investigates the application of the XGBoost algorithm, a gradient boosting technique optimized for performance plus scalability, in discovering fraudulent transactions on the Ethereum network. The model is instructed to distinguish between valid as well as suspicious behaviour based on transactional and behavioural characteristics by examining a dataset of Ethereum transaction records. XGBoost is a popular machine learning algorithm used for a range of tasks, including fraud detection on Ethereum and other blockchain networks. It is a highly effective model due to its performance, flexibility, and ability to handle complex, imbalanced, as well as large datasets. This study emphasises accuracy and precision, also recalling key metrics, demonstrating that XGBoost not only enhances prediction performance but also minimizes false positives over other classification algorithms. Our findings: XGBoost is the optimal model for real-time fraudulent detection in a blockchain context. The accuracy of the XGBOOST algorithm achieves a high level of accuracy.
Biru Rajak, Malik Bader Alazzam, B Karthikeyan, P N V Syamala Rao M · 6 authors
The scale of IoT and IIoT systems developing is growing rapidly since they deploy in critical infrastructure which also created major security issues such as data breaches, unauthorized access, and centralized model lack of trust issues. It is search of this study to formulate a block-chain-based IoT network that provides secure, trustful and corrupted-proof transmission of data. What is new in the approach is the combination of the use of machine learning-based real-time anomaly detection and the Ethereum-based smart contracts in the creation of a decentralized and intelligent security layer. In the offered framework, models XGBoost are trained using the Edge-IIoTset dataset with maximum accuracy of detection of 98%. Compared to traditional centralized and standalone ML-based solutions, the hybrid system is found to be much more efficient in both initial detection (precision), trust enforcement, and resilience. The findings ratify that the framework can be deployed in sensitive IoT/IIoT networks based not only on its ability to identify the source of cyber threats and curb them in real-time without necessitating any update, but also on its potential to increase the transparency and traceability of the network.
Accounting is undergoing a radical transformation due to the integration of traditional information systems with blockchain technology and artificial intelligence. Openness, automation, and smart decision-making will all become a reality via this connection. However, traditional SAIS are typically centralized and do not inherently include blockchain or AI. In this study, Smart Accounting Information System (SAIS) technologies are redefined through the integration of these technologies to enhance transparency, automation, and real-time assurance. Blockchain technology's immutability, traceability, and AI's ability to recognize abnormalities and predict provide a more intelligent and secure auditing process. Conventional accounting methods have several issues, including delayed audits, lack of transparency, fraud, and human mistakes. Existing systems fail to provide intelligent anomaly detection and real-time transaction traceability. Financial reporting and audits need immutable records and proactive analytics. There is an urgent need for a single framework to ensure this requirement and its quick implementation. This study proposes the collaborative blockchain-AI audit trails method (CBAATM) for Smart Accounting Information Systems. This is done due to the difficulties mentioned. AI-powered modules utilize fuzzy inference to dynamically analyze audit risks and Random Forest classifiers to detect real-time fraud. This research project utilizes zero-knowledge proofs and homomorphic encryption to simultaneously handle data aggregation, privacy, and independent audits. Using middleware application programming interfaces makes integration with ERP and AIS systems easy. Throughout the testing process, the model outperforms conventional audits. The methodology, according to statistical research, ensures the detection accuracy ratio of 95%, integrity of the blockchain 99.2% of the time, identifies abnormalities 94.1% of the time, satisfies compliance standards 95.4% of the time, and reduces audit latency by 41.5% compared to other existing models.
The continuous growth of interconnected systems, cloud services, and Internet-of-Things (IoT) devices has expanded the attack surface and intensified modern cyber risks, revealing significant weaknesses in centralized security architectures. Blockchain technology, characterized by decentralized control, immutable record-keeping, and cryptographic verification, offers a robust alternative for strengthening cybersecurity across multiple operational domains. This review analyzes the core technical components of blockchain such as distributed ledgers, consensus mechanisms, and network models and explains their relevance to enhancing security functions. It further examines practical applications in network protection, identity and access management, IoT device security, cloud data governance, and software supply-chain assurance. It highlights emerging research directions, including lightweight blockchain solutions for constrained IoT environments, cross-chain security architectures, artificial intelligence-based blockchain threat analytics, and quantum-resilient cryptographic infrastructures.
A growth in the popularity of interconnected banking systems that make use of the Internet of Things (IoT) has occurred as a result of the rise in the acceptance of digital financial services, which has become increasingly ubiquitous. The Internet of Things devices, despite the fact that they make banking operations more efficient and improve the experience for customers, also make them more vulnerable to hackers on the other hand. It is the case that this is the situation, despite the fact that these devices are advantageous to customers. The goal of this study is to investigate the Internet of Things (IoT) technology in order to determine whether or not it has the capability of enhancing the cybersecurity of financial institutions by means of the implementation of real-time threat detection and protection strategies that are based on blockchain technology: this is the purpose of this research. By deploying sensors that are connected to the Internet of Things in conjunction with analytics, it is feasible to perform continuous monitoring of the activity that occurs on the network, the patterns of transactions, and the interactions that occur between devices. It is projected that as a result of this research, a decentralized security model that makes use of technologies such as the Internet of Things (IoT) and blockchain will be built. This is something that is anticipated to happen. The objective of this study is to ensure that all of these things are achievable in order to guarantee that the flow of data is as transparent as possible, that it is not subject to tampering, and that transaction records cannot be altered. This enables the solution to be implemented. This is accomplished through the use of the IoT. The distributed ledger is the component of blockchain technology that is responsible for guaranteeing that audit trails and transaction data are protected from unauthorized changes or fraudulent activity. This responsibility falls under the purview of the distributed ledger. A superior predictive analysis is produced as a result of the incorporation of algorithms based on artificial intelligence into the ecosystem of the Internet of Things.
<p>The rapid evolution of decentralized finance (DeFi) has brought revolutionary innovations to global financial systems; however, it has also revealed some major security vulnerabilities, especially of smart contracts. Traditional auditing methods and static analysis tools are prone to fail in identifying sophisticated threats, including reentrancy attacks, front-running, oracle manipulation, and honeypots. This review discusses the growing role of machine learning (ML) in enhancing the security of DeFi systems. It provides a comprehensive overview of modern ML-based methods related to the detection of smart contract vulnerabilities, transaction-level fraud detection, and oracle trust assessment. The paper also provides publicly available datasets, necessary toolkits, and architectural designs used for developing and testing these models. Additionally, it provides future directions like federated learning, explainable AI, real-time mempool inspection, and cross-chain intelligence sharing. While it is full of promise, the application of ML in DeFi security is plagued by issues like data scarcity, interoperability, and explainability. This paper concludes by highlighting the need for standardised benchmarks, shared data initiatives, and the integration of ML into development pipelines to deliver secure, scalable, and reliable DeFi ecosystems.</p>
M. Ganesan Alias Kanagaraj, K. Sathiyamurthi, K.K. Karthick, V. Vimalnath · 6 authors
The complexities and inefficiencies that are ever increasing in the payment of insurance claims usually lead to delays, frauds, and disputes, and thus shows the need to allow more transparency and trust to the process. The paper presents a new blockchain-based smart contract platform that automates and simplifies insurance claim settlements through the combination of non-mutable distributed ledger technology and self-executable digital contracts. The presented approach will guarantee transparency since all events associated with claims will be recorded in the blockchain, thereby serving auditable and tamper-proof information which can be accessed by all stakeholders. Smart contracts are coded to ensure that claim conditions are met, triggered automated compensation on meeting policy criteria and the removal of human interaction in the process of decision making, so fraudulent practices and operational expenses are reduced to a minimum. Moreover, this strategy builds customer trust by providing real-time information and decentralized management. The efficiency of the system is measured in terms of transaction speed, cost effectiveness and reduction of fraud and this proves to be much better than the traditional conventional models that are centralized. In this study, the author has identified the revolutionary nature of blockchain to bring change in the insurance industry, a situation where claims can be settled through secure, transparent, and efficient methods.
The demand for such digital financial systems around the world is growing and there's pressure to use the latest technology to make them transparent, safe and not slow. Notable disruptive elements in this area include AI and Blockchain which may change investment desirability all together. In the combination of AI and Blockchain here you get these predictive/ pattern recognition and intelligent automation for such. to what the blockchain would not perfectly deliver as AI does. AI adds predictive/pattern recognition &intelligent automation to what the block-chain does it self as being that Immutable Ledger which is Decentralized (because there's no Central body) & Trustless (when you do this With Smart Contract so everything's Mathematically verified. In conclusion, we have presented an end-to-end intelligent auto-IDMS that fully incorporates AI and Blockchain. The system itself effectively performs in four stages namely Data Ingestion, Prediction Analytics, Decision Execution and Audit Logging. RT TRACKING – including financial & market news, indicators etc from around the world. Pattern recognition, data mining and asset performance prediction - AI models are just a kind of machine learning or deep learning models (only using another black box instead of what already existed) that use raw data feeds in order to diagnose for instance what is actually surprising, or figure out for example what the expected performance would be if there even was one -or look for strange fight-or-flight changes to make based off some lazy portfolio management. The readings are written to a smart contract of some blockchain and invested. All the investment decisions, model updates and transaction logs related to “rights” is written on-chain so the fund is a very transparent and traceable (and of course tamper-proof) application. That is this cool piece of federated learning that is privacy preserving as well as model accountability by fairly banking its data into a private local bank (yet delivering to the global AI models).- Here smart contracts can be use, were software meets KYC like compliance or due-diligence all the while investing logic just farms across a layer automation based on several risk limit profile Assignment and/or performance triggers. It also has a live dashboard to expose the investment thesis and model history, as well as its previous on-chain activity for anyone interested. Experimental results on real-world stock market historical data show that, compared to the traditional existed investment theories, the proposed trading system has higher overall accuracy and lower decision time and transparency. Then there's the blockchain (and friction-but-elseeverything's”) because we'd get to nearly all of operation for next-to-zero opex (old and forced-automatic: intermediaries back. We are laying down the groundwork for an intelligent, trustworthy and scalable financial infrastructure for next-generation investment ecosystems.
Vivekrabinson K, C Yogesh, Indra Kumar M, Hari Suriya K · 6 authors
The rapid progress of generative AI has already seen the rise of highly realistic artificial and deepfake content that has created a problems related to trust on information, privacy, issues related to cyber security loss of general trust. This research introduces TrueSightQ, a unified full stack web application framework through with multimodal detection of AI generated content through use of Quatnum enhancement and Web3 Integration. This system is a hybrid between heuristic and deep learning methods with the added feature of GPU accelerated training combined with quantum advantage classifiers. The trustworthiness and transparency of blockchain technology, as well as IPFS storage and Ethereum Smart contract to improve verification process. The model is further enhanced with modality wise fusion and, decentralized trust based mechanisms for the defense mechanism to adversarial attack. Results show that in general TrueSightQ significantly outperforms standard unimodal detectors overall, with additional gains to verifiability, precision and interpretability demonstrating how the multimodal and decentralized methodology within the model mitigates the issues of AI generated content very efficiently.
Ravisankar M, Raghunandhan V, Senthil Pandi S, T. Kalai Selvi · 6 authors
In the rapidly evolving healthcare sector, the accurate forecasting and secure management of health insurance claims are crucial for both insurance providers and their clients. This study introduces an integrated framework that merges machine learning algorithms with blockchain infrastructure to enhance fraud prevention and optimize the prediction and processing of health insurance claims. The primary objective is to accurately predict claim amounts using advanced regression modeling and to streamline the entire claims administration process via an open, transparent, and decentralized blockchain network. For the predictive component, the XGBoost Gradient Regression algorithm was implemented. To complement this forecasting capability , A blockchain-based system is proposed for managing health insurance claims. This system enables secure collaboration and data exchange among key stakeholders, including hospitals, insurance firms, laboratories, and third-party administrators. The blockchain layer ensures data integrity, transparency, and security, substantially reducing opportunities for fraud, processing delays, and administrative overhead. By uniting the predictive power of machine learning with the trust and efficiency of blockchain, this project offers a robust, intelligent, and secure solution for modern health insurance management.
Abstract The demand for secure, effective, and scalable payment systems has increased due to the rise of Internet-based financial transactions. Through traditional techniques, such as Proof of Work (PoW), conventional financial systems often encounter issues with high transaction latency, concerns about fraud, and excessive energy consumption. These problems are widespread in traditional systems. This research proposes a Secure Hybrid Consensus Protocol (SHCP) with the intention of enhancing the effectiveness, velocity, and reliability of financial transactions based on Blockchain technology. The Proof of Stake (PoS) protocol is combined with the Byzantine Fault Tolerance (BFT) protocol by SHCP. Through the utilization of adaptive prioritization, Bayesian inference, and anomaly recognition, SHCP can incorporate the most advanced fraud detection technology. The SHCP framework uses anomaly recognition to identify fraud with 92% accuracy, 38% faster validation, and 43% less energy than PoW-based systems. The system delivers ~ 7,000 TPS (Transactions Per Second) and a 27% increase in decision risk prediction stability. Anomaly scoring, Bayesian inference, and adaptive prioritization aid fraud detection. These advances enable safe, rapid, and affordable financial transactions, creating a sustainable Blockchain-based payment ecosystem.
Blockchain technology has evolved from its origin as the foundation of cryptocurrencies into a versatile, decentralized framework for secure data management. Its core features include decentralization, immutability, transparency, and cryptographic security which enable trustworthy interactions without centralized authority. This review presents a comprehensive examination of blockchain fundamentals, including architecture, consensus mechanisms, and smart contracts, followed by applications across finance, supply chain, healthcare, IoT, and government systems are highlighted.
This abstract presents a comprehensive concept that leverages the synergy of various cutting-edge technologies to assure confidentiality and integrity of health data. Internet of Things (IoT) sensors are utilized as the primary data source, enabling the continuous monitoring of patients vital signs and health parameters. To ensure the security of this sensitive health data, Blockchain infrastructure is employed. The Blockchain employs a specialized routing protocol called Improved Whale Optimized Routing to efficiently handle data transactions. This routing protocol minimizes latency and maximizes throughput, ensuring the seamless transfer of health data to the Blockchain. The security of the Blockchain is further fortified by Deep Convolutional Neural Network (DCNN) based intrusion detection system. This DCNN model is trained using Distributed Ledger Technology (DLT), which ensures data privacy and integrity by distributing the training process across a network of nodes. This collaborative approach enhances the CNN's ability to identify and respond to potential security breaches in real time. Once the health data is verified as intrusion-free, it is securely stored in the Blockchain using the shortest path routing algorithm. This guarantees that data is efficiently stored, and retrieval is expedited when needed for medical diagnosis or research. This integrated system represents a novel approach for collecting and securely storing health data, providing a robust foundation for the future of healthcare systems. It combines the power of IoT sensors, Blockchain, Deep CNN-based intrusion detection and Distributed Ledger Technology to ensure the highest standards of data security and accessibility in healthcare applications.
This project aims to create, utilize, and evaluate a blockchain-based solution for event ticketing, addressing issues such as fraud, duplicate tickets, excessive fees from intermediaries, and unfair resale practices prevalent in traditional ticketing systems. The project develops a powerful stack of Solidity smart contracts on Ethereum/Polygon blockchains to standardise creation, ownership transfer, and resale policies for tickets. At the same time, a React.js frontend is being developed to create a fast and agile ticket purchase, reservation, and store transfer interface. This interface utilizes Metamask wallet authentication in a Node.js and Express backend, which connects to the blockchain using Web3.js. For decentralization and immutability, ticket metadata is recorded on the IPFS system, and its performance is verified by means of QR code scanning modules integrated into the IoT-based entry gates for real-time validation. The project was deployed on Ganache, and it has been tested on the Polygon Mumbai testnet for performance analysis under real-world conditions. According to the report, there is error-free validation for over 500 concurrent transactions, an 8–12 second average time to confirm a transaction, and an average 70% reduction in counterpart gas fees on Polygon compared to Ethereum. The system addresses user access, transparency, duplicate elimination, and secure resale tracking. This work fills the void between theoretical models and practical solutions, with a demonstration of such a scheme and quantifiable results, proving the applicability of blockchain ticketing systems for transparent and scalable event management that prevents fraud.
Abstract In today’s world, most financial and personal transactions happen online. This makes data security a big concern. To address risks like data breaches, hacking, and identity theft, our project “Blockchain Secure Transaction” aims to create a dependable and decentralized system for secure digital payments. The system uses blockchain technology to ensure transparency and immutability in each transaction, eliminating the need for a central authority. The process starts with user registration, where details are securely stored along with a picture password for better recognition. During login, users must pass both the picture password and a biometric check. This ensures that only the account. Once verified, the user enters the dashboard, where transactions begin through Zero-Knowledge Proof (ZKP) for privacy-preserving verification. Every transaction is validated with smart contracts. If there’s any mismatch or automatically blocks or freezes the transaction. The backend uses Java, while Firebase stores user data securely, and 2 factor.in enables OTP-based authentication. The frontend interface, designed in React (app.jsx), allows smooth navigation across pages. By combining blockchain, smart contracts, biometric authentication, and ZKP, this project provides a secure and user-friendly platform that prevents unauthorized access and builds user trust in digital payment systems Keywords Blockchain, Secure Transaction, Zero Knowledge Proof (ZKP),Smart Contract, Biometric Authentication, Picture Password, Decentralized System, Data Privacy, Transaction Verification, Firebase Integration, 2 factor.in OTP Authentication.
The rapid evolution of cyber threats has exposed fundamental weaknesses in traditional intrusion detection systems, particularly those dependent on centralized architectures vulnerable to data tampering, single-point failures, and delayed threat response. As organizations face increasingly sophisticated attacks, a resilient and transparent framework for detecting and validating abnormal activity has become essential. This study examines the design and effectiveness of a blockchain-based intrusion detection system (BIDS) that leverages distributed consensus, immutable logging, and cooperative threat intelligence to enhance the reliability and responsiveness of security operations. By integrating blockchain technology with anomaly-based and signature-based identification methods, the proposed model establishes a secure environment where intrusion data cannot be altered, suppressed, or manipulated by internal or external adversaries. Through experimental evaluation across simulated network environments, the blockchain-enabled detection model demonstrates significant improvements in event accuracy, traceability, and coordination between participating nodes. The decentralized ledger structure ensures that alerts are validated collectively, reducing false positives and limiting the adversary’s ability to compromise the detection process. The integrity of recorded events also enhances forensic analysis, allowing security teams to reconstruct attack sequences with greater confidence. Additionally, the study reveals that the distributed nature of the system provides high fault tolerance, enabling continuous operation even under attempted denial-of-service conditions or node outages. Performance analysis indicates that blockchain integration does introduce additional computational overhead; however, the trade-off is compensated by the increased transparency, data authenticity, and resistance to insider threats that the system delivers. The research further highlights that smart contracts can automate rule enforcement and improve response mechanisms by triggering protective actions when predefined thresholds are met. This automation contributes to shortening detection-to-response timelines, a critical factor in mitigating fast-moving cyberattacks. Overall, the findings suggest that blockchain-powered intrusion detection represents a promising direction for strengthening network security in decentralized, cloud-based, and large-scale enterprise environments. By combining autonomous threat identification with tamper-proof logging and distributed validation, the proposed approach offers a comprehensive pathway for defending modern digital infrastructures against evolving cyber risks. The study concludes that integrating blockchain technology with intrusion detection principles not only reinforces system resilience but also lays the groundwork for more collaborative, transparent, and secure cybersecurity ecosystems.
The study is grounded on the significant shifts, central values, and increased influence of Blockchain Technology on the industries. It addresses Blockchain Technology starting theoretically as a cryptographic concept of the beginning through to its contribution as a primarycomponent of decentralized computing (Web3). The discussion begins as the key issues are examined, namely, decentralized agreement, cryptographic hashing, and immutability. Such subjects enable the Blockchain Technology to gain trust in cases where mediators were being used in the past. Moreover, the research considers the impacts of Blockchain Technology on such critical industries as Decentralized Finance (DeFi), supply chain management, and decentralized governance (DAOs).
The proliferation of Internet of Things (IoT) applications and on-demand logistics has fostered crowdsourced delivery systems where dynamic coordination among senders, couriers, and receivers enables efficient lastmile logistics. However, centralized dispatching exposes privacy and reliability risks, including single points of failure and leakage of routing and transaction data. Blockchainbased Payment Channel Networks (PCNs) address these limitations by moving frequent interactions off-chain while maintaining verifiable settlement on-chain. This paper presents a blockchain-anchored privacy-preserving path optimization protocol that supports variable-amount multihop payments over PCNs. By combining Pedersen commitments and lightweight zero-knowledge proofs (ZKPs), the protocol verifies transaction correctness without revealing amounts, and employs blind-channel operations to prevent intermediaries from accessing sensitive data. Experimental results show that the proposed scheme achieves strong privacy protection and scalability with low computational cost, making it suitable for blockchain-based IoT delivery environments.