VANet security challenges and solutions: A survey

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Information-centric networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today’s Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, named data networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is intelligent transportation system realized through vehicular ad hoc network (VANET) where vehicles exchange information and content with each other and with the infrastructure. Up to date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community.

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Named Data Networking in Vehicular Ad Hoc Networks: State-of-the-Art and Challenges

Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

V2VR: Reliable Hybrid-Network-Oriented V2V Data Transmission and Routing Considering RSUs and Connectivity Probability

A survey on internet of vehicles: Applications, security issues & solutions

VANET aims to improve safety for all road users. Vehicles exchange safety messages over wireless communication links which are prone to multiple attacks. To enhance the existing security of V2V communications, we propose in this paper a security framework based on vehicles behavior analysis. We define a Hybrid Trust Model (HTM) and a misbehavior detection system (MDS) where a trust metric is assigned to every vehicle depending on its behavior. Using this trust metric, a classification of the vehicles into malicious or honest is done. HTM is based on-the-fly group formation which helps to manage the communication between vehicles and the back-end system by selecting the most trustworthy node as group leader (GL). Vehicles and GL will cooperate with each other within the group and with the back-end system to detect the malicious node and to notify the Misbehavior Authority. The latter takes appropriate actions to limit the consequences of the malicious behaving node. Performance evaluation of HTM and MDS is carried out using Groovenet Simulator. Results show the efficiency of the proposed model to select the trustworthy vehicles and to monitor their behaviors, as well as to classify them and to deactivate the malicious ones.

Trust model for secure group leader-based communications in VANET

In this paper, we consider a Trust Model with Group Leader (GL)-based communication in VANET. This model is used to classify vehicles based on their trustworthiness and elect potential GLs. We propose a security risk assessment methodology and we apply it to our Trust Model. This methodology is used for identifying threats, assessing the risk involved, and defining approaches to mitigate them. The risk assessment includes assessment of the impact and likelihood of occurrence of attacks relevant to the identified threats, evaluation of the Trust Model design principles, validation of the built-in security, and the mitigation actions of attacks. Based on this assessment, we demonstrated the resiliency of the Trust Model to resist to many security attacks.

Security Risk Analysis of a Trust Model for Secure Group Leader-Based Communication in VANET

In this paper, we investigate a security architecture for V2V communication that ensure integrity, confidentiality, anonymity, authenticity and non-repudiation. Based on IEEE 1609.2 Standard, we propose group-based V2V authentication and communication for safety message dissemination with lightweight solution, decentralized via group leaders (GLs), efficient, economical and applicable in real mode. We simulate the existing security solutions using "Estinet" simulator and we show that our group-based authentication proposal performs better than other schemes.

Group-based authentication in V2V communications

Trustworthy communication in VANET Network is essential to provide functional, efficient and reliable traffic safety applications. The main concern arises on how to maintain only the trustworthy participants and revoke the misbehaving ones. In this paper, we propose a new framework for the certificate revocation process within VANET. This process can be activated by the Misbehavior Detection Systems (MDSs) running within vehicles and the Misbehavior Authority (MA) within the infrastructure, which identifies and excludes misbehaving vehicles to guarantee the long-term functionality of the network. These MDSs rely on the trust evaluation for participating vehicles which is updated continuously based on their behaviors. Therefore, the revocation is done periodically through geographical Certificate Revocation List (CRL) which specifies the certificates of all revoked vehicles within a specific area. This results in a lightweight solution for CRL management and distribution within a modular and secure infrastructure based on Public Key Infrastructure (PKI), group formation and trust evaluation. Simulation scenarios and risk analysis were carried out showing the advantages of the proposed revocation framework.

Hamssa Hasrouny

Papers

VANet security challenges and solutions: A survey

Trust model for secure group leader-based communications in VANET

Security Risk Analysis of a Trust Model for Secure Group Leader-Based Communication in VANET

Group-based authentication in V2V communications

Misbehavior detection and efficient revocation within VANET