Search Results (80)

The Internet of Vehicles (IoV) is a rapidly evolving technology that provides real-time connectivity, enhanced road safety, and reduced traffic congestion; however, its inherently open communication channels expose it to serious security and privacy threats. In 2021, Chaudhry proposed SMEP-IoV, a lightweight message authentication protocol designed to satisfy essential security requirements. This paper presents a comprehensive security analysis of SMEP-IoV and reveals several serious vulnerabilities. Specifically, sensitive credentials are stored in plaintext without tamper-resistant protection, and both authentication and session key derivation depend directly on these credentials. These structural flaws allow an adversary to extract the stored secrets, generate valid authentication messages, and derive the established session key, enabling vehicle impersonation and session key disclosure attacks. Moreover, compromise of long-term secrets facilitates key compromise impersonation attacks. It also fails to ensure anonymity and perfect forward secrecy. To address these issues, we propose an enhanced authentication protocol for resource-constrained IoV environments, leveraging a three-factor authentication mechanism combined with lightweight cryptographic primitives. Formal security analyses using BAN logic, Tamarin, and ProVerif confirm its resilience against known attacks, while NS-3 simulations validate its scalability, high throughput, and low End-to-End Delay (E2ED). The results highlight the protocol as a robust, efficient, and scalable solution for large-scale IoV deployments. Full article

In the Internet of Vehicles (IoV), connected vehicles generate high-load perception tasks with large-scale and multimodal sensitive data, imposing strict requirements on latency, computing, and privacy. Existing solutions still suffer from high task service latency and privacy risks. To address these issues, this paper proposes an integrated framework that jointly considers multi-flow task offloading, adaptive privacy preservation, and latency-aware resource incentive mechanism. Specifically, we propose a Location-Aware and Trust-based (LA-Trust) dual-node task offloading algorithm based on deep reinforcement learning (DRL), which treats pre-partitioned subtasks as multiple parallel flows and enables flow-level collaborative offloading optimization across neighboring nodes, allows subtask data uploading and processing to proceed concurrently, and incorporates node security into decision making. To further enhance privacy protection, a Distribution-Aware Local Differential Privacy (DA-LDP) algorithm is designed to adaptively inject artificial noise according to data heterogeneity, balancing privacy protection and task execution accuracy. In addition, a Delay-Cost Reverse Auction (DC-RA) algorithm is proposed to further reduce latency by introducing wireless channel modeling between idle vehicles and edge nodes into the incentive mechanism. Experimental results show that the proposed framework improves task execution accuracy by 38% and reduces offloading cost, delay, incentive cost, and auction communication latency by 64.41%, 64.64%, 19%, and 44%, respectively, while more than 60% of tasks are offloaded to high-trust nodes. Full article

With the rise of smart cities, technology has enabled more efficient urban management. A key part of this is the Internet of Vehicles (IoVs), which connects vehicles to smart city systems to improve transportation safety and efficiency. This integrated system enables wireless connection between vehicles, allowing for the sharing of essential traffic information. However, with all this connectivity, there are growing concerns about IoV security and privacy. This paper presents a new privacy-preserving authentication scheme for Autonomous Vehicles (AVs) in the IoV field using physical unclonable functions (PUFs). This scheme employs a bilinear pairing-based encryption technique that supports search over encrypted data. The primary aim of this scheme is to authenticate AVs inside the IoV architecture. A novel PUF design generates random keys for our authentication technique, hence boosting security. This dual-layer security strategy safeguards against a range of cyber threats, including identity fraud, man-in-the-middle attacks, and unauthorized access to personal user data. The PUF design will guarantee the true randomness of the AVs’ users’ secret keys. To handle the large amount of data involved, we use hardware acceleration with different Field-Programmable Gate Arrays (FPGAs). Our examination of privacy and security demonstrates the achievement of the defined design goals. The proposed authentication framework was fully implemented and validated on FPGA platforms to demonstrate its hardware feasibility and efficiency. The integrated heterogeneous PUF achieves an average reliability exceeding $98.5\%$ across a wide temperature range, while maintaining near-ideal randomness with an average Hamming weight of $49.7\%$ over multiple challenge sets. Furthermore, the uniqueness metric approaches $49.9\%$, confirming strong inter-device distinguishability among different PUF instances. The complete authentication architecture was synthesized on Nexys-100T, Zynq-104, and Kintex-116 devices, where the design utilizes less than $80\%$ of slice Look-Up Tables (LUTs), under $27\%$ of on-chip memory resources, and below $16\%$ of DSP blocks, demonstrating low hardware overhead. Full article

The convergence of Vehicular Ad Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks. Full article

The increasing adoption of electric vehicles (EVs) and the integration of 5G/6G networks are driving the demand for secure, intelligent, and interoperable charging infrastructure within the Internet of Vehicles (IoV) ecosystem. Electric Vehicle Charging Stations (EVCS) face growing cyber–physical threats, including spoofing, data injection, and firmware tampering, risking user privacy, grid stability, and EVCS reliability. While artificial intelligence (AI), blockchain, and cryptography have been applied in cybersecurity, comprehensive solutions tailored to EVCS challenges, such as real-time threat mitigation and scalability, are often lacking. This paper addresses these critical cybersecurity gaps by presenting a comprehensive overview of novel strategies for enhancing EVCS security through the Internet of Digital Twins (IoDT) technology. The primary objective is to evaluate advanced frameworks that synergize digital twins with artificial intelligence, blockchain, and quantum-resistant cryptography. Through systematic literature analysis, global threat assessments, and review of international standards, this study identifies key attack vectors and their impacts on EVCS. Key findings demonstrate that digital twin-driven solutions facilitate real-time monitoring, anomaly detection, predictive threat mitigation, and secure system governance. This review offers actionable insights for researchers, industry stakeholders, and policymakers to strengthen the cybersecurity and resilience of next-generation electric mobility infrastructure, addressing challenges like scalability and implementation barriers. Full article

Connected and autonomous vehicles, along with the expanding Internet of Vehicles (IoV), are increasingly exposed to complex and evolving cyberattacks. Consequently, Intrusion Detection Systems (IDS) have become a vital component of modern vehicular cybersecurity. Federated Learning (FL) enables multiple vehicles to collaboratively train detection models while keeping their local data private, providing a decentralized alternative to traditional centralized learning. Despite these advantages, FL-based IDS frameworks remain vulnerable to attacks. To address this vulnerability, we propose an explainable federated intrusion detection framework that enhances both the security and interpretability of IDS in connected vehicles. The framework employs a Deep Neural Network (DNN) within a federated setting and integrates explainability through the Shapley Additive Explanations (SHAP) method. This Explainable Artificial Intelligence (XAI) component identifies the most influential network features contributing to detection decisions and assists in recognizing anomalies arising from malicious or corrupted clients. Experimental validation on the CICEVSE2024 and CICIoV2024 vehicular datasets demonstrates that the proposed system achieves high detection accuracy. Moreover, the XAI module improves transparency and enables analysts to verify and understand the model’s decision-making process. Compared with both centralized IDS models and conventional federated approaches without explainability, the proposed system delivers comparable performance, stronger resilience to attacks, and significantly enhanced interpretability. Overall, this work demonstrates that integrating FL with XAI provides a privacy-preserving and trustworthy approach for intrusion detection in connected vehicular networks. Full article

As electric vehicles (EVs) gain popularity, the existing public charging infrastructure is struggling to keep pace with the rapidly growing demand for the immediate charging needs of EVs. V2V power trading has gradually attracted widespread attention and development. EVs need to transmit sensitive information, such as transaction plans, through communication entities in the Internet of Vehicles (IoV). This could lead to leaks of sensitive information, thereby threatening the fairness of transactions. In addition, due to the differences in the cryptographic systems of entities, communication between entities faces challenges. Therefore, a privacy-preserving scheme for V2V double auction power trading based on heterogeneous signcryption and IoV is proposed. Firstly, a heterogeneous signcryption algorithm is designed to realize secure communication from certificateless cryptography to identity-based cryptography. Secondly, the scheme employs a pseudonym mechanism to protect the real identities of EVs. Furthermore, a verification algorithm is designed to verify the information sent by EVs and ensure the traceability and revocation of malicious EVs. The theoretical analysis shows that the proposed scheme could serve common security functions, and the experiment demonstrates that the proposed scheme reduces communication costs by about 14.56% and the computational cost of aggregate decryption by 80.51% compared with other schemes in recent years. Full article

With the rapid deployment of intelligent transportation systems (ITS), the Internet of Vehicles (IoV) has become an increasingly vital component in the development of smart cities. However, the openness of IoV also gives rise to critical issues such as message security and identity privacy. Consequently, addressing message authentication in the IoV environment is a fundamental requirement for ensuring its sustainable and stable evolution. Firstly, this paper proposes an adaptive traffic authentication strategy (ATAS) By integrating traffic flow dynamics evaluation, traffic status scoring, time sensitivity assessment, and comprehensive strategy decision-making, the scheme achieves an effective balance between authentication efficiency and security in IoV scenarios. Secondly, to tackle the high overhead and security issues caused by multiple message transmissions in large-scale IoV application scenarios, this paper proposes a secure message transmission and authentication method based on the cloud-edge-client collaborative architecture. Leveraging aggregate message authentication code (AMAC) technology, this method validates both the authenticity and integrity of messages, effectively reducing communication overhead while maintaining reliable authenticated transmission. Finally, this paper builds an IoV co-simulation experimental environment using the SUMO 1.19.0, OMNeT++ 6.0.3, and Veins 5.0.0 software platforms. It simulates the interactive authentication process among vehicles, Road Side Units (RSUs), and the cloud platform, as well as the effects of traffic response strategies under different scenarios. The results demonstrate the potential of IoV authentication technology in improving traffic management efficiency, optimizing road resource utilization, and enhancing traffic safety, providing strong support for the secure communication and efficient management of IoV. Full article

The Internet of Vehicles (IoV) relies on Vehicle-to-Everything (V2X) communications to enable cooperative perception among vehicles, infrastructures, and devices, where Vehicle-to-Infrastructure (V2I) links are crucial for reliable transmission. However, the openness of wireless channels exposes IoV to eavesdropping, threatening privacy and security. This paper investigates an Intelligent Reflecting Surface (IRS)-assisted interweave Cognitive IoV (CIoV) network to enhance physical layer security in V2I communications. A non-convex joint optimization problem involving spectrum allocation, transmit power for Vehicle Users (VUs), and IRS phase shifts is formulated. To address this challenge, a heterogeneous multi-agent (HMA) Mamba RainbowDQN algorithm is proposed, where homogeneous VUs and a heterogeneous secondary base station (SBS) act as distinct agents to simplify decision-making. Simulation results show that the proposed method significantly outperform benchmark schemes, achieving a 13.29% improvement in secrecy rate and a 54.2% reduction in secrecy outage probability (SOP). These results confirm the effectiveness of integrating IRS and deep reinforcement learning (DRL) for secure and efficient V2I communications in CIoV networks. Full article

Internet of Vehicles (IoV) serves as the data support for intelligent transportation systems, and the information security of the IoV is of paramount importance. In view of the problems of centralized processing, easy information leakage, and weak anti-interference ability in traditional vehicle networking systems, this paper proposes a blockchain architecture suitable for IoV forensics scenario. By leveraging the decentralized, distributed storage and tamper-proof capabilities of blockchain, it solves the privacy protection and data security issues of the system. Considering the threat of quantum computing to the encryption technology in traditional blockchain, this paper integrates lattice cryptography and ring signatures into digital signature technology, achieving privacy protection and traceability of the signer’s identity. To enhance the efficiency of lattice-based cryptographic algorithms, the DualRing technology is introduced, which reduces the computational time and storage consumption of ring signatures. Theoretical analysis has proved the correctness, anonymity, unlinkability, and traceability of the proposed scheme, which is applicable to the IoV forensics system. Simulation comparisons demonstrated that the proposed scheme significantly improves computational efficiency and reduces storage overhead. When the number of ring members is 256, the signature and verification times require only 65.76 ms and 21.46 ms, respectively. Full article

With the rapid development of intelligent connected vehicle technology, false data injection (FDI) attacks have become a major challenge in the Internet of Vehicles (IoV). While deep learning methods can effectively identify such attacks, the dynamic, distributed architecture of the IoV and limited computing resources hinder both privacy protection and lightweight computation. To address this, we propose FedIFD, a federated learning (FL)-based detection method for false data injection attacks. The lightweight threat detection model utilizes basic safety messages (BSM) for local incremental training, and the Q-FedCG algorithm compresses gradients for global aggregation. Original features are reshaped using a time window. To ensure temporal and spatial consistency, a sliding average strategy aligns samples before spatial feature extraction. A dual-branch architecture enables parallel extraction of spatiotemporal features: a three-layer stacked Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies, and a lightweight Transformer models spatial relationships. A dynamic feature fusion weight matrix calculates attention scores for adaptive feature weighting. Finally, a differentiated pooling strategy is applied to emphasize critical features. Experiments on the VeReMi dataset show that the accuracy reaches 97.8%. Full article

Generative artificial intelligence (GAI) has emerged as a transformative force in the Internet of Vehicles (IoV), addressing limitations of traditional AI such as reliance on large labeled datasets and narrow task applicability. This paper aims to systematically review recent advances in applying GAI to the IoV, with a focus on training, decision-making, and security. We begin by introducing the fundamental concepts of vehicular networks and GAI, laying the groundwork for readers to better understand the subsequent sections. Methodologically, we adopt a structured literature review, covering developments in synthetic data generation, dynamic scene reconstruction, traffic flow prediction, anomaly detection, communication management, and resource allocation. In particular, we integrate multimodal GAI capabilities with 5G/6G-enabled edge computing to support low-latency, reliable, and adaptive vehicular network services. Our synthesis identifies key technical challenges, including lightweight model deployment, privacy preservation, and security assurance, and outlines promising future research directions. This review provides a comprehensive reference for advancing intelligent IoV systems through GAI. Full article

The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent problems regarding data privacy, security from adversarial attacks, and the handling of available resources. This paper introduces Fed-DTB, a new dynamic trust-based framework for FL that aims to overcome these challenges in the context of IoV. Fed-DTB integrates the adaptive trust evaluation that is capable of quickly identifying and excluding malicious clients to maintain the authenticity of the learning process. A performance comparison with previous approaches is shown, where the Fed-DTB method improves accuracy in the first two training rounds and decreases the per-round training time. The Fed-DTB is robust to non-IID data distributions and outperforms all other state-of-the-art approaches regarding the final accuracy (87–88%), convergence rate, and adversary detection (99.86% accuracy). The key contributions include (1) a multi-factor trust evaluation mechanism with seven contextual factors, (2) correlation-based adaptive weighting that dynamically prioritises trust factors based on vehicular conditions, and (3) an optimisation-based client selection strategy that maximises collaborative reliability. This work opens up opportunities for more accurate, secure, and private collaborative learning in future intelligent transportation systems with the help of federated learning while overcoming the conventional trade-off of security vs. efficiency. Full article