Towards the Safety of Intelligent Transportation: A Survey on the Security Challenges and Mitigations in Internet of Vehicles (IoV)

Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.

with recent advancements in technology, different devices that are used in our day-to-day lives are able to connect to internet, communicate and exchange messages with each other. This new paradigm is called “Internet of Things” and is providing services such as smart hospitals, smart city, home automation etc. “Internet of Things” has changed conventional small scale Vehicular Ad-hoc Network (VANET) to a highly scalable, manageable internet based “Internet of Vehicle” (IoV). IoV is a vehicular network model consisting of vehicles, users and other smart devices connected to network and aims to provide various safety as well as entertainment services. Vehicles in IoV system are equipped with different sensors that collect different types of data and send it to computation unit for computation and analysis, based on which directions and other information is sent to each vehicle. We present a model overview of IoV system in this paper. Security in IoV becomes of paramount importance as any system failure directly affects user safety. In this paper, we discuss security issues, various security attacks and their countermeasures from IoV perspective. We also propose an authentication mechanism for Vehicle-to-Infrastructure (V2I) communication in IoV.

The internet of vehicles (IoV), a variant of the traditional VANET, allows real-time data exchange between vehicles, roadside units, parking, and city infrastructure. Nevertheless, the IoV poses many security concerns due to its open nature. Traditional security solutions may not address all the IoV security risks and provide complete protection. Therefore, it is critical to establish trust and to identify dishonest nodes. As a result, trust management-based techniques are also required to improve IoV security. This paper proposes federated learning with a blockchain approach for trust management (FBTM) in IoV. Thus, a vehicular trust evaluation is designed to improve the data acquired for the federated learning model learning process. Moreover, a novel blockchain-based reputation system is developed to guarantee the storage and the share of global federated learning models. In the meanwhile, proof of reputation consensus is proposed to evaluate the roadside units operating as aggregators in the IoV network. Simulation results demonstrate that the proposed scheme is effective for IoV security.

ABSTRACTRecent advancements in intelligent automobiles and artificial intelligence (AI) have sparked significant interest in Internet of Vehicles (IoV) technology. While conventional machine learning methods have been widely used to enhance IoV security, they are not well‐equipped to handle the complexities of IoV communications or prevent malicious vehicles from influencing the ML model formation process. These limitations highlight the urgent need for more effective IoV security solutions to ensure the integrity and reliability of vehicular communication networks. To address these challenges, we propose a novel blockchain‐based trust‐federated learning (FL) framework for IoV attack detection. This framework incorporates a trust‐based FL model to enhance the security of IoV communications. We introduce a unique trust value system for vehicles, which improves the reliability of the FL model by selectively using data from trusted vehicles. Additionally, we employ a two‐level blockchain approach: the InterPlanetary File System (IPFS) for off‐chain local model storage and a dedicated blockchain managed by RSUs for global model aggregation and storage. Experimental results demonstrate the effectiveness of our solution in strengthening IoV communication security.

The Internet of Vehicles (IoV) is becoming an interesting topic among researchers and it has emerged as a rapidly advancing field within Vehicular Ad-hoc Networks, facilitating intelligent communication between vehicles and the cloud through the integration of Internet of Things (IoT) technologies. The IoV surroundings face serious challenges due to the highly interrelated nature of vehicles and infrastructure in certifying privacy and security. Traditional approaches to authentication lack the strength required to protect against developing fears, leaving systems vulnerable to attacks. This survey addresses the gap by employing formal analysis approaches to prove authentication protocols, targeting to reinforce safety and confidentiality in IoV systems. The IoV communication model consists of Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Vehicle-to-Personal Devices, and Vehicle-to-Cloud. Smart automobiles are equipped with cameras, radars, on-board units, and sensors to help reduce the number of accidents by giving drivers or autonomous vehicles up-to-date information on roads, traffic signals, and other pertinent entities. As human lives are at risk, security and privacy in the IoV communication paradigm are critical and cannot neglected. Security and privacy breaches may cause accidents because the attacker can inject false information into the system as the communication channel is open and unsecured. The researchers proposed many authentication protocols to provide secure communication between IoV entities. Although surveys on IoV security and privacy issues deal with communication and computation costs, they lack formal analysis of the authentication protocols. This survey reviews the informal analysis and formal analysis methods used by various authentication protocols. Furthermore, the challenges and future work are also included in this survey.

The Internet of Vehicles (IoV) is becoming an interesting topic among researchers and it has emerged as a rapidly advancing field within Vehicular Ad-hoc Networks, facilitating intelligent communication between vehicles and the cloud through the integration of Internet of Things (IoT) technologies. The IoV surroundings face serious challenges due to the highly interrelated nature of vehicles and infrastructure in certifying privacy and security. Traditional approaches to authentication lack the strength required to protect against developing fears, leaving systems vulnerable to attacks. This survey addresses the gap by employing formal analysis approaches to prove authentication protocols, targeting to reinforce safety and confidentiality in IoV systems. The IoV communication model consists of Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Vehicle-to-Personal Devices, and Vehicle-to-Cloud. Smart automobiles are equipped with cameras, radars, on-board units, and sensors to help reduce the number of accidents by giving drivers or autonomous vehicles up-to-date information on roads, traffic signals, and other pertinent entities. As human lives are at risk, security and privacy in the IoV communication paradigm are critical and cannot neglected. Security and privacy breaches may cause accidents because the attacker can inject false information into the system as the communication channel is open and unsecured. The researchers proposed many authentication protocols to provide secure communication between IoV entities. Although surveys on IoV security and privacy issues deal with communication and computation costs, they lack formal analysis of the authentication protocols. This survey reviews the informal analysis and formal analysis methods used by various authentication protocols. Furthermore, the challenges and future work are also included in this survey.

In recent years, with the rapid development of autonomous driving technology, Internet of Vehicles (IoV) has strong requirements for the security of user data, especially user privacy data. In this paper, we introduce the security issues of data in current IoV system and review privacy security in IoV. Recent studies indicate that the security of IoV system is largely caused by data security. Data encryption can solve system security, but it will consume more system resources. In addition, the user authorization issues in IoV system also affect the user data security. Therefore, we propose an IoV system architecture, which is based on the IoT system architecture. To solve user data security issues, we present an IoV authorization system, which can implement hierarchical control and management of different levels of data to achieve the goal of load balancing in IoV system.

Hybrid Approaches for Intrusion Prediction in IoV (Internet of Vehicles) Authors : Naveen Joshi1, Dr Nirmal Kaur2 1Research Scholar, SBBSU, Jalandhar, naveenjoshi84@gmail.com 2 Associate Professor, SBBSU, Jalandhar Abstract The Internet of Vehicles (IoV) is a burgeoning field integrating smart vehicles into a connected ecosystem, enabling Vehicle-to-Everything (V2X) communications. However, this connectivity increases vulnerability to cyber threats, necessitating robust intrusion detection systems (IDS). This paper explores hybrid approaches combining signature-based and anomaly-based detection methods to enhance security in IoV. We discuss the architecture, algorithms, and performance metrics of hybrid IDS, emphasizing their advantages and potential challenges. A detailed analysis of hybrid IDS implementations is presented, supported by diagrams and empirical data. Keywords: Internet of Vehicles (IoV), Intrusion Detection Systems (IDS), Machine Learning (ML), Deep Learning (DL), Hybrid Approaches, Cyber security, Vehicle-to-Everything (V2X), Anomaly Detection, Predictive Security, Network Security.

Hybrid Approaches for Intrusion Prediction in IoV (Internet of Vehicles) Authors : Naveen Joshi1, Dr Nirmal Kaur2 1Research Scholar, SBBSU, Jalandhar, naveenjoshi84@gmail.com 2 Associate Professor, SBBSU, Jalandhar Abstract The Internet of Vehicles (IoV) is a burgeoning field integrating smart vehicles into a connected ecosystem, enabling Vehicle-to-Everything (V2X) communications. However, this connectivity increases vulnerability to cyber threats, necessitating robust intrusion detection systems (IDS). This paper explores hybrid approaches combining signature-based and anomaly-based detection methods to enhance security in IoV. We discuss the architecture, algorithms, and performance metrics of hybrid IDS, emphasizing their advantages and potential challenges. A detailed analysis of hybrid IDS implementations is presented, supported by diagrams and empirical data. Keywords: Internet of Vehicles (IoV), Intrusion Detection Systems (IDS), Machine Learning (ML), Deep Learning (DL), Hybrid Approaches, Cyber security, Vehicle-to-Everything (V2X), Anomaly Detection, Predictive Security, Network Security.

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.

Integration of sensing, communication, computing, and intelligence (ISCCI) represents a pivotal advancement in B5G and 6G technologies, offering transformative potential for the Internet of Vehicles (IoV). As IoV systems become increasingly integral to intelligent transportation and autonomous driving, these systems also face escalating security challenges across multiple layers, including physical, network, application, and system dimensions. (1) This paper comprehensively surveys these security issues, systematically analyzing the threats encountered at each layer and proposing targeted countermeasures to mitigate risks. (2) Furthermore, the paper explores future trends in IoV security, emphasizing the roles of 6G networks, blockchain technology, and digital twins in addressing emerging challenges. (3) Finally, based on a comprehensive review of current research and insights, this paper aims to serve as a foundational reference for advancing secure and sustainable IoV ecosystems.

Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology’s decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework’s effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems.

Information security of Internet of Vehicles (IoV) has attracted much attention in recent years. In view of security vulnerabilities existed in automobiles, many countries launch guidelines and cybersecurity standards concerning IoV security and plenty of new techniques have been applied to combat threats. In this paper, a variety of attacks on IoV are summarized and classified, then artificial intelligence and game theory based security countermeasures for IoV are highlighted, and their protection mechanisms are illustrated. Finally, a few application cases of artificial intelligence and game theory based security strategies for IoV is analyzed, aiming to provide helpful reference for the development of IoV security techniques.

This chapter focuses on the applications and challenges of the Internet of Vehicles (IoV) and how Natural language processing is used in safety applications in IoV. The Internet of Things (IoT) is used to identify the internet of vehicles. The tremendous growth in the smart automotive sectors has recently led to a huge rise in interest in Internet of Vehicles (IoV) technology. IoV is used to connect objects, vehicles, and surroundings so that data and information may be transferred between networks. It also lets cars transmit and gather information about other vehicles and roadways. By easing traffic congestion, enhancing traffic management, and assuring road safety, IoV is introduced to improve the experience of road users. The challenges and problems that the contemporary IoV system faces are covered in this study. How to manage the privacy of huge groups of data and cars in IoV systems is one of the critical issues that researchers need to deal with. IoV networks may benefit from the numerous clever solutions provided by artificial intelligence (AI) technology to handle all the queries and problems. There is a deep connection between IoT and AI. Similarly, IoV being a subset of IoT and natural language processing (NLP) being a subset of AI are also deeply connected. Without NLP, it is difficult to run the voice control systems in IoV. The hands-free interface, which is provided by NLP, benefits the IoV in many ways. NLP techniques can be used to improve safety concerns in IoV. For instance, using sensory data from the surrounding area, NLP may be used to analyze driving behavior and the surroundings in order to prevent traffic accidents. This chapter consists of a detailed survey on IoV, with its applications and challenges, and NLP technologies that can be used for safety applications.