B Santhosh Kumar, P. Penchala Prasad, M. Raghavendra Reddy
Abstract Alzheimer’s disease is a neurodegenerative disorder that affects millions of individuals worldwide, making early diagnosis through Magnetic Resonance Imaging a significant clinical necessity. Existing medical image analysis techniques often suffer from limitations associated with inadequate preprocessing, reduced sensitivity to subtle abnormalities in the hippocampus and cortex, poor generalization across heterogeneous MRI acquisition systems, and insufficient mechanisms for secure medical data management. To address these challenges, this research proposes an integrated framework combining the Internet of Medical Things (IoMT), Artificial Intelligence, and blockchain technology for secure and efficient Alzheimer’s disease monitoring. The proposed framework employs Feature Pooling VGG16 (FPVGG16) for discriminative feature extraction, while feature selection is optimized using the Wave Search Binary Waterwheel Plant Optimization algorithm. Subsequently, a feature-selective Coordinated Xception-based Convolutional Spatial Network (CXCSN) is utilized for accurate disease classification. Blockchain technology is incorporated to provide secure, tamper-resistant, and privacy-preserving management of patient information and MRI records. Experimental evaluations conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets validate the effectiveness of the proposed framework, achieving accuracies of 99.31% and 99.21%, precisions of 99.28% and 99.25%, and recalls of 99.18% and 99.14 %, respectively. The results indicate that the proposed framework provides an effective solution for secure, reliable, and highly accurate Alzheimer’s disease diagnosis and monitoring.
Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, ageinvariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations.
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.
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder profoundly affecting memory and cognitive functions for which an early and precise diagnosis is essential to achieve timely intervention and disease management. Magnetic Resonance Imaging (MRI) is an important tool for detecting structural changes in the brain such as hippocampal shrinkage and ventricular enlargement, which can be correlated with Alzheimer’s disease’s stages of progression. In this work, we present a framework that couples deep learning-based Alzheimer’s MRI classification with blockchain-supported image authenticity verification. Our experimental setup compares five classification approaches, Xception, Long Short-Term Memory (LSTM) networks, ResNet50, Random Forest, and Gradient Boosting across different training durations. The best performing model is integrated to the local IPFS node and Ethereum smart contract through Ganache. This comparative investigation examines the balance of accuracy, efficiency, and training time in a variety of model designs. It also illustrates the viability of a secure, decentralized framework for both diagnostic accuracy and data integrity through blockchain.
Nazia Azim, Khair Ul Burria, Muhammad Zain Asghar, Zeeshan Raza · 6 authors
Ethereum blockchain is the market leading platform for decentralized applications and smart contracts that have powered the new age of financial ecosystem. In order to improve security and performance, identify influential nodes, and understand network dynamics on Ethereum it is critical to identify influential nodes in Ethereum. This study explore machine learning techniques for discovery of these nodes using graph based algorithms, centrality measures and clustering methods. It studies the impact of a node in terms of frequency of usage, connectivity and computational power for a node. Finally, this study compare performance of proposed methodology combining supervised learning and graph neural networks to their traditional counterparts and demonstrate approach outperforms existing methods. The study demonstrate that highly influential nodes engage in unique patterns of behavior, which are detectable and categorizable. This study contribute to understanding of the network structure of Ethereum, along with a scalable approach to monitoring and optimising blockchain ecosystems. Moreover the study discuss the implications for network robustness, fraud detection and protocol enhancements, and demonstrate the promise of machine learning for blockchain analytics.
Abdullah Ayub Khan, Abdul Khalique Shaikh, Roobaea Alroobaea, Abdullah M. Baqasah · 7 authors
Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250 ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.
The industrial internet of things (IIoT) expanded fast as physical devices and systems were connected to the internet. However, this interconnectedness made IIoT systems vulnerable to hackers. Intrusion detection systems (IDSs) were put in place to detect and prevent such assaults. Nonetheless, attackers might circumvent IDSs by forging identities or interfering with recorded data. The article intended to improve IIoT security by achieving system confidentiality, integrity, availability, scalability, performance, and security. For IIoT security, the article developed a secure federated learning access control framework (SecureFLACF) linked with a blockchain-based IDS. SecureFLACF used blockchain to secure data collected by IDS, AES-256 encryption to secure stored data, zero-knowledge proof (ZKP) to validate user identities and manage data access, and a federated learning access control framework (FLACF) to train a machine learning model for intrusion detection. SecureFLACF developed as a viable solution for improving IIoT security, providing strong assurances for IDS data and access control using blockchain’s tamper-proof structure and AES-256 encryption. Furthermore, FLACF’s design allows private machine learning model training, ensuring data privacy as well as model fidelity. The framework’s usefulness was highlighted by its application in real-world circumstances, making it a cost-effective option for organisations of all sizes. This method not only strengthened IIoT systems against a wide range of cyber threats, but also stressed their dependability as a safeguard. SecureFLACF exhibited considerable promise for improving IIoT security across several dimensions by encapsulating practicability, cost-effectiveness, and dependability.
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, Dong‐Seong Kim
The growing complexity of distributed industrial IoT systems heightens cybersecurity risks, exposing the limitations of centralized ML-based intrusion detection. Federated Learning (FL) enables decentralized, privacy-preserving model training but remains susceptible to adversarial threats and system-level failures. This study introduces PureChain, a decentralized ledger using a proof-of-authority and association (PoA 2 ) consensus mechanism to enhance FL-based IDS security. The study offers insight into the mathematical model of the PureChain-enhanced FL, which integrates blockchain-inspired consensus protocols for collaborative intrusion detection across organizations, ensuring data privacy while providing tamper-proof logs and automated responses through smart contracts. It incorporates dynamic fault tolerance, poisoning resistance, and privacy preservation with FL, enhancing security and performance in decentralized systems. Experimentation with varying client subsets demonstrates its adaptability with a TPS range of 312 . 5 − 1178 . 3 and a low latency range of 0 . 0008484 − 0 . 0032 . The framework ensures comprehensive security, reliability, and privacy, providing a scalable solution for decentralized, secure systems.
We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.
Blockchain technology has emerged as a transformative force across a multitude of sectors, offering decentralized, transparent, and tamper-proof solutions to conventional problems in data management, finance, supply chain, healthcare, and beyond.Initially popularized through cryptocurrencies, blockchain has since evolved into a broader infrastructure supporting smart contracts, decentralized applications (dApps), and Web3 ecosystems.This survey provides a comprehensive overview of blockchain technology, outlining its fundamental principles including distributed ledgers, consensus mechanisms, cryptographic security, and decentralization.We critically examine various blockchain architectures such as public, private, and consortium blockchains, and explore their relative strengths and limitations.The paper further delves into current trends, emerging use cases, scalability challenges, interoperability issues, and security concerns.By synthesizing recent academic and industry developments, this survey aims to provide researchers and practitioners with a holistic understanding of blockchain's capabilities, current limitations, and future directions.
The Internet of Medical Things (IoMT) integrates interconnected medical devices and sensors to enable continuous patient monitoring and real-time healthcare delivery. Despite its transformative potential, IoMT systems face critical challenges related to data privacy, interoperability, latency, and security vulnerabilities inherent in centralized cloud architectures. Blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, has emerged as a promising solution to secure sensitive medical data while ensuring transparency and compliance with regulations such as HIPAA and GDPR. Concurrently, reinforcement learning (RL) techniques, especially advanced deep RL algorithms, facilitate intelligent, adaptive task offloading in fog-cloud computing environments to optimize latency, energy consumption, and resource allocation. This survey synthesizes twenty recent studies addressing blockchain-enabled privacy-preserving frameworks and RL-based task offloading mechanisms in IoMT. It critically evaluates architectural designs, cryptographic innovations including zero-knowledge proofs and quantum-resistant signatures, and RL methodologies for dynamic resource management. Key research challenges identified include the lack of standardized interoperability protocols across heterogeneous IoMT devices and blockchain platforms, the computational overhead of quantum-resistant cryptography on resource-constrained devices, and the opaque nature of RL models hindering clinical trust. Future research directions emphasize developing unified communication standards, lightweight post-quantum cryptographic schemes tailored for IoMT edge devices, and explainable RL frameworks to foster clinical adoption. Ultimately, this comprehensive analysis delineates a pathway toward robust, scalable, and secure IoMT ecosystems capable of delivering efficient, privacy-preserving healthcare services in complex digital infrastructures.
Stakeholders throughout the food supply chain-from farmers to consumers-can gain safe, unchangeable views of the origin, movement, and processing of food through the distributed and immutable ledger of blockchain technology. Traceability and accountability are thereby made possible by this transparency, ensuring food items meet safety and quality guidelines and allowing consumers to make informed decisions. Our proposal for blockchain-based agri-food traceability follows the Bitcoin SHA-256 hash mechanism to provide a safe and impregnable way of monitoring agricultural products across the supply chain. Data is collected, annotated, and given a SHA-256 hash before getting written across decentralized blockchain networks. As the blockchain is fully visible and unchangeable, data integrity is assured so that diverse stakeholders can track the provenance of any product. In addition, the automated and easy use of smart contracts creates further accessibility for participants and clients, thereby ensuring trust and transparency in the agri-food system.
This paper proposes the design of electronic medical record system supported by multimedia communication based on blockchain technology. Blockchain technology ensures the secure storage and sharing of patient information through distributed ledger and smart contract algorithm. In this system, smart contracts are used to automatically execute cross-institutional data access control and audit functions to ensure the transparency and compliance of data access. At the same time, this paper introduces multimedia communication technology to support the efficient transmission and sharing of medical data, especially in diagnostic images, videos and voice. Simulation results show that the electronic medical record system based on blockchain has significantly improved data processing efficiency, security and reliability compared with traditional systems.
Abdullah Alsaleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma · 5 authors
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. We introduce the anisotropic-residual capsule hybrid Gorilla Badger optimized network (Aniso-ResCapHGBO-Net) framework for detecting brain tumors in a privacy-preserving, decentralized system used by many healthcare institutions. ResNet-50 and capsule networks are incorporated to achieve better feature extraction and maintain the structure of images' spatial data. To get the best results, the hybrid Gorilla Badger optimization algorithm (HGBOA) is applied for selecting the key features. Preprocessing techniques include anisotropic diffusion filtering, morphological operations, and mutual information-based image registration. Updates to the model are made secure and tamper-evident on the Ethereum network with its private blockchain and SHA-256 hashing scheme. The project is built using Python, TensorFlow and PyTorch. The model displays 99.07% accuracy, 98.54% precision and 99.82% sensitivity on assessments from benchmark CT imaging of brain tumors. This approach also helps to reduce the number of cases where no disease is found when there is one and vice versa. The framework ensures that patients' data is protected and does not decrease the accuracy of brain tumor detection.
As the cryptocurrency market continues to evolve, phishing scams are considered one of the most deceptive forms of fraud. Currently, most existing Ethereum phishing detection methods rely on traditional machine learning or graph representation learning, mainly depending on local statistical and structural features. This can lead to insufficient utilization of transaction graph data across different scales. To address this challenge, we propose Multi-transaction-view Graph Attention Network (MTvGAT), which fully leverages edge features between nodes at different scales and discovers relationships between nodes. Two types of graphs are used to model Ethereum transactions: global views and local views. Global views are constructed by partitioning the complete transaction graph using graph clustering algorithms and inputting them into MTvGAT to obtain global view representations. For each target node, a local view is constructed by sampling K-hop neighbors from the transaction network. Importantly, attention coefficients are calculated between nodes, and edge coefficients are obtained by fusing edge features and attention coefficients, utilizing spatial structure and edge coefficients to enable the phishing detection model to access multi-view sources of information. Experimental results demonstrate that the multi-view graph attention network outperforms existing algorithms in detecting Ethereum phishing scams datasets.
Hui Dou, Xuewei Wang, Mian Ahmad Jan, Haiwei Sang · 5 authors
Within the realm of 6G Internet of Vehicles (6G-IoV), Federated Learning (FL) has become a notable machine learning framework, providing a decentralized method to protect data privacy while allowing cooperative model training. Specifically, with 6G technology, FL will benefit from ultra-low latency, high reliability and massive connectivity, enabling real-time model updates and efficient data sharing in the 6G-IoV ecosystem. However, FL faces challenges like the single points of failure and potential privacy leakage from data providers. To tackle the aforementioned challenges, we propose a blockchain-based trustworthy verifiable FL scheme for 6G-IoV, that is, AVBFL. Firstly, we introduce blockchain technology to address the issue of decentralization by storing transactions on-chain. Furthermore, to protect the privacy of local gradients, we utilize the Burmester-Desmedt (BD) multi-party key agreement protocol to negotiate a shared key and encrypt the gradients with the AES encryption algorithm. We also sign transactions using the ECDSA signature algorithm. Additionally, we design a time-sensitive Proof of Stake (TPoS) consensus mechanism based on Newton’s cooling law to boost participants’ enthusiasm for training and select the miner with the highest stake to mine the block. Finally, experiments have demonstrated the effectiveness of AVBFL. In the presence of malicious nodes, the average accuracy rate is increased by 71.8% compared to the VFL scheme and by 8.6% compared to the VBFL scheme.
P. Chinnasamy, B. Subashini, Ramesh Kumar Ayyasamy, Ajmeera Kiran · 7 authors
The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways. Our study aims to propose a novel electronic educational document management technique using a blockchain-based fuzzy feed-forward convolutional temporal neural network that detects malicious users. Here, the training is carried out based on NLP analysis in document word weight indexing. This document management access control is based on role-based access with simulated remora swarm optimisation. In order to identify malicious users, this suggested system logs access requests on the blockchain and authenticated users. The findings demonstrate that this suggested architecture performs as intended in every case. The experimental analysis is based on a malicious user detection dataset regarding Prediction accuracy, Mean average precision, F-measure, Latency, QoS, Contract execution time, and Throughput. Based on dataset feature analysis, the proposed B-FCTNN_SRSO achieved a prediction accuracy of 98%, a mean average precision (MAP) of 95%, and an F1 score of 97%, with a latency of 96%. Additionally, based on blockchain security analysis, the B-FCTNN_SRSO attained a QoS of 97%, a precision of 94%, and a throughput of 96%.
Mochan Fan, Zonghang Li, Gang Sun, Hongyang Du · 6 authors
Benefiting from the rapidly expanding Internet of Things (IoT) data and powerful computing devices, AI-generated content (AIGC) trains models with vast knowledge to provide automated content generation services. Sharing knowledge through the ciphertext-policy attribute-based encryption (CP-ABE) algorithm is beneficial for training high-quality AIGC models to offer better services. However, existing CP-ABE sharing schemes often involve untrusted third parties, which can result in issues such as knowledge deletion, unverifiable access, and single points of failure. To address these challenges, some blockchain-based sharing schemes have been developed. However, they still face privacy leakage problems. In this paper, we propose SecureShare, a secure and verifiable knowledge sharing scheme based on a consortium blockchain for AIGC services. We begin by outlining a blockchain knowledge sharing architecture and optimizing the Delegated Proof of Stake (DPOS) committee node selection method to ensure that entities can achieve verifiable access control. Additionally, to achieve fine-grained access to knowledge ciphertext while preserving privacy, we propose a CP-ABE scheme with Policy Hiding, attribute privacy preservation, and Revocation, referred to as PHR-CP-ABE. PHR-CP-ABE ensures the privacy of access policies and attributes, and users whose attributes have been revoked cannot decrypt knowledge further. A case study on Dall-E clearly illustrates the operational mechanism of the proposed scheme. We provide theoretical analysis of the security of both the AIGC knowledge sharing scheme and PHR-CP-ABE. Through extensive performance analysis and comparisons with existing schemes, our approach demonstrates significant advantages in terms of computation and communication overhead.
Blockchain technology has become a significant paradigm which has been utilized to transform various industries and applications. Its decentralized, transparent, and secure nature has led to widespread adoption in diverse fields such as finance, healthcare, supply chain management, and the Internet of Things (IoT). This paper presents a comprehensive survey of blockchain technology, focusing on three key aspects: consensus algorithms, data storage mechanisms, and blockchain architectures. We provide a detailed overview of various consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authentication (PoAh), and Practical Byzantine Fault Tolerance (PBFT), discussing their mechanisms, advantages, limitations, and challenges. Furthermore, we explore different data storage mechanisms, such as on-chain, off-chain, and hybrid storage, analyzing their implications for scalability, security, and efficiency. We also delve into various blockchain architectures, including single, dual, and multi-blockchain architectures, examining their suitability for different applications. This survey provides a holistic understanding of blockchain technology, highlighting its potential, challenges, and future directions. It serves as a valuable resource for researchers, developers, and practitioners interested in exploring and leveraging the capabilities of blockchain.
Che, Zheng, Taoyu Li, Meng Shen, Hanbiao Du · 5 authors
The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, which restrict their practical applicability. In this paper, we propose StealthLink, a novel framework that addresses these limitations through cross-task domain-invariant feature learning. Our key innovation lies in transferring knowledge from the well-studied domain of blockchain anomaly detection to the data-scarce task of mixing transaction tracing. Specifically, we design a MixFusion module that constructs and encodes mixing subgraphs to capture local transactional patterns, while introducing a knowledge transfer mechanism that aligns discriminative features across domains through adversarial discrepancy minimization. This dual approach enables robust feature learning under label scarcity and distribution shifts. Extensive experiments on real-world mixing transaction datasets demonstrate that StealthLink achieves state-of-the-art performance, with 96.98\% F1-score in 10-shot learning scenarios. Notably, our framework shows superior generalization capability in imbalanced data conditions than conventional supervised methods. This work establishes the first systematic approach for cross-domain knowledge transfer in blockchain forensics, providing a practical solution for combating privacy-enhanced financial crimes in decentralized ecosystems.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.