Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

Intelligent Transportation System (ITS) and Internet of Vehicles (IoV) allows the vehicles to disseminate and exchange various messages among nearby vehicles in the network. It includes road safety, road accidents, location sharing, driver assistance, navigation, collision warning, traffic data, and toll payment. Moreover, the exchange of these messages is carried out in an insecure channel. Hence, it leads to several issues during transmission of these messages in resource constrained IoV networks. It includes reliable data dissemination, dynamic topology, mobility of the vehicles, vehicle user’s privacy, and lightweight authentication mechanism. The existing Vehicle to Vehicle (V2V) data dissemination techniques have limitations in security attacks such as side channel, device stolen, cloning attacks, high computational overhead, throughput, and high communication cost. In this article, we propose P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV: Privacy Preserving Lightweight Secure (V2V) Data Dissemination Scheme for IoV environment using lightweight cryptographic primitives such as SHA-256, concatenation and XoR operation. We evaluate P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV in terms of security and vehicle user’s privacy against several security attacks. We do an in-depth security analysis of the P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV by using informal security analysis and provides security proofs. We also perform a comparative analysis of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV with the existing state-of-the-art IoV environment using various performance parameters. The test-bed experimental result shows that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> IoV outperforms the existing V2V data dissemination technique in terms of computational cost, communication overhead, and energy consumption.

The popularization of the Internet of Vehicles (IoV) technology has not only brought many conveniences but also caused security issues such as distributed denial of service (DDoS) attacks. Using deep learning methods to deal with DDoS attacks has achieved remarkable results. However, to improve the effectiveness of the model, a large amount of high-quality data is needed. This data is often scattered among different vehicle operating companies or organizations, making it difficult to train effective attack detection models by centralizing the data. To solve this problem, this paper proposes a DDoS attack detection method based on reinforced federated learning in the IoV environment. This method applies reinforcement learning to the aggregation process of federated learning to optimize the DDoS attack detection algorithm. In this method, the fog node server of the IoV serves as a participant in learning, uses local data to conduct reinforcement learning, and uploads the trained model parameters to the edge server of the IoV for aggregation. By learning the adaptability of the fog node model to the environment, reinforced federated learning can improve the accuracy of the DDoS attack detection model in terms of unbalanced data distribution and model convergence efficiency. Experiments were carried out using the public DDoS attack dataset. The experimental results showed that the proposed method exhibited excellent performance in the IoV environment, providing new ideas and methods for the research of IoV security.

Internet of Vehicles (IoV) represents a complex and dynamic environment of collaboration and data exchange between vehicles, infrastructure devices, pedestrians, cloud nodes, and other components. Establishing a trust relationship between such objects has proven to be unavoidable for allowing secure access to services such as routing, road safety consigns, and traffic data accessibility. However, security and privacy issues become more complex in such a context characterized by the large volume of data and the geographical dispersion and the dynamicity of involved objects. In this work, we present security and privacy issues encountered in an IoV environment and recently proposed solutions. Moreover, we propose an approach to improve IoV security and privacy while respecting latest advancements in IoV architectures which are based on edge, fog, and cloud layers. Practically, we consider the fog paradigm, which has recently emerged, to handle authentication and authorization of IoV devices at the edge level. Thus, a trust authority is deployed at every fog node along with an interfog authority allowing the handover and collaboration between fog nodes. This approach improves the manageability of low-level devices, thus allowing detecting threats at the fog node level, as well as allows the isolation of threats protecting the overall system from infections.

The Internet of Vehicles (IoV) is a complex and dynamic mobile network system that enables information sharing between vehicles, their surrounding sensors, and clouds. While IoV opens new opportunities in various applications and services to provide safety on the road, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed, decentralized, dynamic, and mobile infrastructures of the IoV. Forensic investigators will face challenges while identifying necessary pieces of evidence from the IoV environment, and collecting and analyzing the evidence. In this article, we propose TrustIoV – a digital forensic framework for the IoV systems that provides mechanisms to collect and store trustworthy evidence from the distributed infrastructure. Trust-IoV maintains a secure provenance of the evidence to ensure the integrity of the stored evidence and allows investigators to verify the integrity of the evidence during an investigation. Our experimental results on a simulated environment suggest that Trust-IoV can operate with minimal overhead while ensuring the trustworthiness of evidence in a strong adversarial scenario.

With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)—a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.

In the Internet of Things (IoT), the advancement of the new modern era of Internet‐off driving, known as the Internet of Vehicles (IoV), is aimed at the Intelligent Transportation System (ITS) to improve road safety and traffic. Therefore, IoV is better for traffic management. But perhaps, the main challenges in this type of network are that timely decision to make adequate decisions by a driver under certain conditions of rapid change in topology, high vehicular mobility, and frequent link failures is hard to improve road safety. Therefore, an optimized and congestion‐free route is required to collect real‐time data from vehicles. Thus, some of the latest bioinspired optimization routing algorithms in the IoV environment are presented in this study. Hence, monitoring speed limits, pollution checks, and emergency responses to traffic accidents should also be considered while performing vehicle routing to avoid traffic problems. In the previous decades, many route‐directing protocols for the IoV environment have been proposed that can handle the requirements of reliability and security. But such routing of protocols suffers from high complexity and scalability limitations in big‐scale networks, routing overlays, etc. Therefore, their strengths, weaknesses, and critical characteristics are compared using various criteria for such algorithms. Then, a suggestion is made to propose a prescribed combined model of a multimodular bioinspired approach to IoV routing. Finally, the main future directions of research in this sector are highlighted.

In the Internet of Vehicles (IoV), numerous potential applications have come up with the use of the Internet of Things (IoT)-empowered smart devices. In IoV, vehicles, roads, street signs and traffic lights can accordingly adjust to changing conditions in order to assist drivers, and also to improve safety, ease congestion and pollution reduction. Since various entities in an IoV environment make communications over public channels, there are potential security threats. To deal with such serious threats, we design a new blockchain-assisted certificateless key agreement protocol for IoV in smart transportation context, called Block-CLAP. In Block-CLAP, through authentication key management, traffic-centric data reach to a cluster head (CH) and then to its nearby road-side unit (RSU) securely using the established secret keys. A cloud server (CS) then securely collects the information from its attached RSUs and create the transactions. Later, the transactions are formed into blocks by the CS in a Peer-to-Peer (P2P) cloud servers network, and the blocks are verified and added through voting-based consensus algorithm in the blockchain. The detailed security analysis through formal, informal and formal security verification, and comparative study show that Block-CLAP provides superior security and has low communication and computational overheads as compared with other existing competing authentication schemes in the IoV environment. Finally, the blockchain-based implementation of Block-CLAP has been performed to measure computational time needed for a varied number of transactions per block and also for a varied number of blocks mined in the blockchain.

The increasing complexity of the Internet of Vehicles (IoV) necessitates robust Intrusion Detection Systems (IDS) to protect against cyberattacks. Recent studies show a 60% rise in attacks targeting vehicular communication systems, with 45% attributed to attacks. Existing IoV-based IDS methods face challenges such as imbalanced datasets and limited feature extraction capabilities, which hinder accurate threat detection. The proposed IoV-Net framework addresses these issues by integrating comprehensive data preprocessing, where noise and redundancies in the Canadian Institute for Cybersecurity collected IoV 2024 (CICIoV2024) dataset are eliminated, followed by dimensionality reduction. For feature extraction, the Transfer Learning Adopted Hybrid Inception-ResNetV2 (TLA-HIR) model is employed, leveraging its superior ability to capture both local and global patterns in IoV data. To tackle class imbalance, the Adaptive Synthetic Minority Over-Sampling (ASMOS) technique is introduced, enhancing the dataset with synthetic samples for underrepresented classes, thus preventing overfitting. Finally, the Machine Learning-based Categorical Boosting (MLCB) Classifier is implemented, ensuring high accuracy in attack classification by utilizing gradient boosting and efficient handling of categorical features. This methodology, applied to the CICIoV2024 dataset with the feature analysis on three cases: binary, decimal, and hexadecimal, promises improved detection rates for sophisticated IoV-based attacks with an accuracy of 99.84%, 99.88%, and 99.88%, respectively.

The fast development of information technology and communication includes Internet of Things (IoT), cloud computing, Internet vehicle blockchain technology, V2G technology, and artificial intelligence. The Internet of Vehicles (IoV) is one of the application of IoT. Recently, Vasudev et al. have published a paper [1] in “IEEE Transactions on Vehicular Technology” with DOI: 10.1109/TVT.2020.2986585 in IoV environment [1]. In this paper, we reviewed Vasudev et al.’s protocol and find some of the design flaws and security features like forward secrecy, vehicle server impersonation attack, insider attack, and others weaknesses. Further, we provide some valuable suggestions for future research for authentication and key agreement protocols in V2V communication.KeywordsCryptanalysisDesign flawsV2V communicationInternet of Vehicles

Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access.

The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.

The Internet of Vehicles (IoV) can deliver services for intelligent transportation systems. However, it typically relies on the exchange of sensitive information, including passwords and personal data, which makes it vulnerable to many security risks, such as phishing attacks, which have largely increased in the last decade. Cryptography-based security measures look as a form of protection to preserve sensitive information, but they can be bypassed by inside attackers and, in addition, they increase the burden on the network. To tackle the aforementioned issues, in this paper, we present an approach to control phishing attacks in the IoV environment, based on Machine Learning (ML) and Deep Learning (DL) techniques.

Today’s Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure. To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability.

Advanced connection and autonomous features are being made possible by the Internet of Vehicles (IoV), which is causing a revolution in transportation. Strong security measures are required, however, because the prevalence of connected devices also increases the likelihood of cyberattacks and data breaches. This study introduces a new method for protecting IoV networks, which combines Blockchain technology with Quantum Key Distribution (QKD), creating a security architecture with two layers. Internet of Vehicles (IoV) technologies enable autonomous driving and real-time data exchange by connecting vehicles to infrastructure and one another. These advancements make things safer and more efficient, but they also put sensitive information at risk of cyberattacks. Modern security measures are essential since traditional encryption methods are becoming more and more insecure. To provide encryption that is theoretically unbreakable, the suggested system uses QKD to create and distribute cryptographic keys based on principles of quantum mechanics. To improve trust and transparency, blockchain technology is used to record these keys and any subsequent transactions in an immutable, distributed ledger. A hybrid architecture, with QKD securing the key exchange and Blockchain ensuring the integrity and authenticity of the communication, is designed as part of the integration process. Improved security and speed have been shown in simulations and prototype implementations of the QKD-Blockchain architecture in IoV networks. By preventing eavesdropping and key interception, the QKD technique kept the communication channel secure. With an average delay of only about 2 milliseconds, QKD performed admirably and was well below the permitted range for real-time vehicular communications. On average, validation durations for transactions were 5 milliseconds, which was a little overhead due to blockchain integration. The system efficiently handled up to 10,000 transactions per second without affecting security or performance, proving that it can serve massive IoV networks, according to scalability testing. Under high-load scenarios, the framework maintained consistent performance and security, proving its robustness in stress tests. Together, QKD and Blockchain provide a scalable and trustworthy option for future vehicular communication networks, and these results show how feasible and robust it is to use them to protect IoV systems. An intriguing approach to the security issues plaguing IoV systems is the integration of QKD with Blockchain technology. An unparalleled level of protection against cyber threats is provided by the dual-layered system, which guarantees strong encryption and data integrity. This fresh method may lead to improved and more trustworthy IoV networks by establishing new benchmarks for secure vehicular communication. In order to optimize the implementation and tackle any new issues that may arise, more research and development should be conducted.