Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 2245-2277· 0 citations· 58 references
TL;DR
A blockchain-enabled cybersecurity framework for ITS that integrates distributed ledger technology with secure vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything communication is proposed that incorporates identity management, consensus-based validation, trust evaluation, secure data sharing, intrusion detection, and privacy-preserving mechanisms.
Abstract
The rapid evolution of Intelligent Transportation Systems (ITS) has resulted in highly connected transportation ecosystems involving vehicles, roadside infrastructure, traffic management centres, cloud platforms, edge devices, and users. Although connectivity improves traffic efficiency, road safety, and intelligent mobility, it simultaneously increases exposure to cyber threats such as data manipulation, identity spoofing, Sybil attacks, denial-of-service attacks, replay attacks, malicious node behaviour, and privacy violations. Blockchain technology provides a promising cybersecurity foundation for ITS through decentralised trust management, immutable data storage, cryptographic authentication, transparent transaction verification, and smart-contract-based access control. This paper proposes a blockchain-enabled cybersecurity framework for ITS that integrates distributed ledger technology with secure vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything communication. The framework incorporates identity management, consensus-based validation, trust evaluation, secure data sharing, intrusion detection, and privacy-preserving mechanisms. Particular emphasis is placed on lightweight consensus mechanisms and edge-assisted blockchain architectures to address the latency, computational, storage, and scalability constraints of vehicular environments. The proposed framework provides a systematic architecture for improving data integrity, authentication, accountability, privacy, and resilience against cyberattacks while supporting intelligent and safety-critical transportation services.
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
The proposed BlockSafeNet framework achieved significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures, providing a positive impact on the SC ecosystem.
The Internet of Vehicles (IoV) is revolutionizing intelligent transportation systems by ubiquitous connectivity of vehicles, roadside infrastructure, pedestrians, edge/cloud platforms, and smart-city services. With the IoV evolving towards highly connected, autonomous and data-driven mobility ecosystems, it needs to meet challenging requirements for low latency, scalability, interoperability, security, privacy and trust. This paper presents a comprehensive cross-layer approach for intelligent, secure and privacy-preserving IoV systems. It is built upon an analytical framework and systematically studies the perception, communication, edge/cloud computing, blockchain-enabled trust and application layers of IoV technologies. In addition, the paper presents an in-depth review of the enabling techniques such as machine learning (ML), deep learning (DL), reinforcement learning (RL), federated learning (FL), blockchain, cybersecurity mechanisms, digital twins, edge computing, 6G integration, and resource allocation. Moreover, it discusses the interplay and trade-offs between intelligence, security, privacy, computation, latency, and scalability. The survey also covers other significant challenges like intrusion detection, decentralized authentication, privacy-preserving learning, blockchain overhead, semantic interoperability, post-quantum security, and standardized datasets. This study is intended to serve as a structured reference for the development of scalable, trustworthy, and intelligent IoV systems by highlighting state-of-the-art techniques, open research gaps, and future directions.
Mohanad Alayedi, Ahmad M. Jaradat· Machine Learning and Knowled...· 0 citations
The smart grid systems are increasingly becoming digital where the system is taking a new direction into greater operational efficiency and real time energy management. The increased number of Internet of Things (IoT) devices and communication networks has widened the area of attack as well, thus smart grids are vulnerable to more sophisticated cyber attacks like false data injection, denial-of-data manipulation and service attacks. The traditional centralized security controls are not sufficient because of the limitation of single point failure, scalability limitations, and demand decentralized and resilient security controls. The use of blockchain technology in this regard has become a potential solution in order to improve the level of security by utilizing the transparent, decentralized, and immutable features of the technology. To provide cybersecurity approaches of smart grid systems, a taxonomical structure of blockchain technology is proposed in this paper. Besides that it shows the categorization of the various techniques into secure data management, secure energy trading, device authentication, data integrity assurance and communication protection. A detailed analysis of existing works was undertaken to determine their efficiency based on the security, scale and complexity of implementation. The results of the review describe some of the major challenges, such as latency and energy overhead and provide future research opportunities on effective smart grid security frameworks.
F. Basheer, Hari Gobind Pathak, Meena Malik et al.· International Conference on...· 0 citations
Decentralized Cybersecurity Mesh Architecture (DCSMA) is a new cybersecurity architecture designed to secure distributed networks, particularly Internet of Things (IoT) environments. Unlike traditional approaches that rely on centralized control or isolated security silos, it leverages blockchain technology to enable fully decentralized security management, eliminating single points of failure and improving coordination among security components. In this paper, a practical implementation of DCSMA is presented and evaluated in an Internet of Vehicles (IoV) environment. The system is developed using a hybrid simulation framework that combines Carla for IoV scenarios and Ganache for blockchain emulation. It integrates key technologies, including Decentralized Identity (DID), Secure Multi-Party Computation (SMPC), smart contracts, and Event-Driven Architecture (EDA), to support secure communication and distributed policy enforcement. The proposed system is evaluated through multiple case studies covering data protection, communication and policy enforcement, and threat detection. Performance is analyzed using metrics such as latency, memory consumption, scalability, throughput, and transaction success rate, in addition to machine learning-based anomaly detection. The results demonstrate that the proposed implementation achieves efficient decentralized security with low overhead and effective threat detection, highlighting its suitability as a scalable and resilient cybersecurity solution for IoV and distributed IoT systems.
With the fast pace of digitalization of transport infrastructure, smart elements and technologies have been integrated into railway networks and smart port operations, improving the degree of automation, real-time monitoring, predictive maintenance, and efficient logistics management. While all these developments enhance the efficiency and reliability of operations, they create new cybersecurity challenges with the increasing interconnections between cyber and physical elements. Such cyber threats as malware, distributed denial-of-service (DDoS) attacks, GPS spoofing, insider attacks, false data injection, and sensor tampering can impact transportation operations, tamper with operational data, and pose a threat to public safety for critical infrastructures. This research introduces an intelligent cyber-physical security framework for integrated railway and smart port operations, which integrates artificial intelligence (AI), Internet of Things (IoT) technologies, edge computing, blockchain-based secure communication, modeling and simulation with a digital twin, and decision support functions to create an all-encompassing cybersecurity architecture for the integrated transportation systems. The proposed framework was able to continuously retrieve diverse operational data from the railway signaling systems, smart port automation equipment, industrial control systems, IoT sensors, surveillance systems, communication networks, and logistics databases for real-time security analysis. Intelligent threat detection and classification are achieved with the help of advanced AI algorithms, and blockchain technology offers secure authentication and trusted information sharing and tamper-resistant data management. Edge computing can minimize communication delays by analyzing security data near operational assets, while digital twin technology can be used for predictive security risk evaluation, cyberattack simulation, infrastructure monitoring, and assessing infrastructure resilience. The experimental evaluation of the performance shows the ability to detect intrusions with better accuracy, false alarm rate, throughput, response time, and AUC values than the traditional machine learning and deep learning models.
A. S. Anshad, Kunal G. Srinivas, Sahana S. Kumar et al.· International journal of com...· 0 citations
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