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Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and $F$ -score, respectively.

Recently, Vehicular Ad-hoc Networks (VANETs) are in the initial stage of their deployment. Protecting drivers' privacy is one of the most critical problems raised by researchers. VANET users would not accept building location profiles of the movements of their vehicles. To that effect, solving this issue is of significant concern. Pseudonym switching and radio silence techniques are among the widely accepted privacy protection solutions in the literature. Radio silence intervals have a considerable impact on the efficiency of the proposed schemes. In this paper we will evaluate the performance of some known pseudonym switching schemes under different radio silence periods.

Vehicular Ad Hoc Networks (VANETs) allow the share of various sorts of information between vehicles in a collaborative way. High delivery ratio and low latency are two main objectives of VANET routing schemes. Unlike other types of vehicles, Public Buses (PB)s that constitute the Bus Rapid Transit system have wide coverage, fixed routes, and regular services. Inspired by these unique features, several bus-based backbone protocols have been proposed. In this paper, we present an enhanced version of the Bus-based Routing Technique (BRT). BRT uses a learning process to determine the required time (the temporal distance) for each data transmission to RoadSide-Unit (RSU)s. As a routing strategy, BRT nodes (buses) use the previously learned temporal distances as a metric when selecting the next-hop. In the absence of a better candidate toward the destination, nodes switch into the store and forward DTN mechanism. In this work, we present a new variant of BRT that we call the Delay Sensitive Bus-based Routing Technique (DSBRT). Our technique suggests an enhanced learning process to optimize the latency. The performance of BRT and DS-BRT has been evaluated using the network simulator NS-2 and SUMO. The simulation results show that our improved BRT version outperforms BRT in terms of end-to-end delay and packet delivery ratio.

Vehicular ad-hoc network (VANET) provides a unique platform for vehicles to intelligently exchange critical information, such as collision avoidance messages. It is, therefore, paramount that this information remains reliable and authentic, i.e., originated from a legitimate and trusted vehicle. Trust establishment among vehicles can ensure security of a VANET by identifying dishonest vehicles and revoking messages with malicious content. For this purpose, several trust models (TMs) have been proposed but, currently, there is no effective way to compare how they would behave in practice under adversary conditions. To this end, we propose a novel trust evaluation and management (TEAM) framework, which serves as a unique paradigm for the design, management, and evaluation of TMs in various contexts and in presence of malicious vehicles. Our framework incorporates an asset-based threat model and ISO-based risk assessment for the identification of attacks against critical risks. The TEAM has been built using VEINS, an open source simulation environment which incorporates SUMO traffic simulator and OMNET++ discrete event simulator. The framework created has been tested with the implementation of three types of TMs (data oriented, entity oriented, and hybrid) under four different contexts of VANET based on the mobility of both honest and malicious vehicles. Results indicate that the TEAM is effective to simulate a wide range of TMs, where the efficiency is evaluated against different quality of service and security-related criteria. Such framework may be instrumental for planning smart cities and for car manufacturers.

Vehicular ad-hoc network (VANET) and internet-of-vehicles (IoV) are complex networks which provide a unique platform for vehicles to communicate and exchange critical information (such as collision avoidance warnings) with each other in an intelligent manner. Thus, the information disseminated in the network should be authentic and originated from legitimate vehicles. Creating a trusted environment in the network can enable the vehicles to identify and revoke malicious ones. Trust is an important concept in VANET and IoV to achieve security in the network, where every vehicle equipped with an appropriate trust model can evaluate the trustworthiness of the received information and its sender. This chapter discusses trust in both VANET and IoV and identifies various trust models developed for VANET and IoV. The contribution of this chapter is threefold. First, the authors present a detailed taxonomy of trust models in VANET and IoV. Second, they provide current trends in the domain of trust management specifically for VANET and IoV, and finally, they provide various open research directions.

Vehicular Ad-hoc NETworks (VANET) have revolutionised the intelligent transportation systems. Indeed, they enable vehicles to communicate with each other and with the infrastructure, and they facilitate several vital applications in real-time. Several trust models have been proposed to ensure a secured VANET and the authenticity, integrity and reliability of information exchanged in the network. The concept of trust models is to introduce and implement trust explicitly in the vehicular nodes. In this paper, we propose a lightweight evaluation methodology to evaluate trust models, specifically designed for VANET. Our study focused on the three categories of VANET trust models (TMs): Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Combined Trust Models (CTM). Our simulations evaluate the efficiency of the trust models and compare them against several trust related parameters: false positive rate, precision and accuracy level in detecting malicious nodes. Since the scope of this research work is to evaluate the performance of TMs under adversary conditions, we have considered on-off attacks which can act as man-in-the-middle to drop and delay transmitted packets.

Vehicular Ad-hoc NETwork (VANET) is a vital transportation technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with each other and with the surrounding infrastructure in real-time to avoid accidents and enable comfortable driving experience.To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.

Vehicular Ad-Hoc Network (VANET), a vital component of Intelligent Transportation Systems (ITS) technology, relies on communication between dynamically connected vehicles and static Road Side Units (RSU) to offer various applications (e.g., collision avoidance alerts, steep-curve warnings and infotainment). VANET has a massive potential to improve traffic efficiency, and road safety by exchanging critical information between nodes (vehicles and RSU), thus reducing the likelihood of traffic accidents. However, this communication between nodes is subject to a variety of attacks, such as Man-In-The-Middle (MITM) attacks which represent a major risk in VANET. It happens when a malicious node intercepts or tampers with messages exchanged between legitimate nodes. In this paper, we studied the impact on network performance of different strategies which attackers can adopt to launch MITM attacks in VANET, such as fleet or random strategies. In particular, we focus on three goals of MITM attacks&mdash, message delayed, message dropped and message tampered. The simulation results indicate that these attacks have a severe influence on the legitimate nodes in VANET as the network experience high number of compromised messages, high end-to-end delays and preeminent packet losses.

Vehicular Networks (VANET) are the largest real-life paradigm of ad hoc networks which aim to ensure road safety and enhance drivers’ comfort. In VANET, the vehicles communicate or collaborate with each other and with adjacent infrastructure by exchanging significant messages, such as road accident warnings, steep-curve ahead warnings or traffic jam warnings. However, this communication and other assets involved are subject to major threats and provide numerous opportunities for attackers to launch several attacks and compromise security and privacy of vehicular users. This paper reviews the cyber security in VANET and proposes an asset-based approach for VANET security. Firstly, it identifies relevant assets in VANET. Secondly, it provides a detailed taxonomy of vulnerabilities and threats on these assets, and, lastly, it classifies the possible attacks in VANET and critically evaluates them.

Vehicular Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. As VANET is characterised with several unique features such as the high mobility, random distribution and short connection times, etc. establishing security becomes very challenging, and identifying security threats and possible attacks become very critical. Thus, identifying, managing and exploiting risk across the different components of VANET have become increasingly important to ensure the success and the robustness of the VANET components and its applications. As VANET include different contexts, the risk posed by particular attack may have different impact, i.e., the risk caused by an attack in rural and urban area may have different consequences on the network. To this end, we propose a novel context-based risk assessment methodology to identify attacks in different contexts of VANET. The main aim of the paper is twofold: 1) First, we identified vulnerabilities, threats and attacks on important assets in VANET, and (2) secondly, we proposed a novel context-based risk assessment approach in VANET to identify the risks encountered by attacks in VANET in various contexts.

Vehicular Ad Hoc Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. This paper introduces the future emerging technology, i.e., Vehicular Cloud Networking (VCN) where vehicles and adjacent infrastructure merge with traditional internet clouds to offer different applications ranging from low sized applications to very complex applications. VCN is composed of three types of clouds: Vehicular cloud, Infrastructure cloud and traditional Back-End (IT) cloud. We introduced these clouds via a three tier architecture along with their operations and characteristics. We have proposed use cases of each cloud tier that explain how it is practically created and utilised while taking the vehicular mobility in consideration. Moreover, it is critical to ensure security, privacy and trust of VCN network and its assets. Therefore, to describe the security of VCN, we have provided an in-depth analysis of different threats related to each tier of VCN. The threats related to vehicular cloud and infrastructure cloud are categorized according to their assets, i.e., vehicles, adjacent infrastructure, wireless communication, vehicular messages, and vehicular cloud threats. Similarly, the Back-End cloud threats are categorized into data and network threats. The possible implications of these threats and their effects on various components of VCN are also explained in detail.

Cloud computing has been widely adopted across the IT industry due to its scalable, cost-effective, and efficient services. It has many applications in areas such as healthcare, mobile cloud computing (MCC), and vehicular ad hoc networks (VANET). Vehicular cloud networks (VCN) is another application of cloud computing which is a combination of cloud and VANET technologies. It is composed of three clouds named vehicular cloud, infrastructure cloud, and traditional IT cloud. In this chapter, the three clouds involved in VCN are presented using a three-tier architecture, and the security issues related to each tier are described in detail. After describing the detailed architecture of VANET, their components, and their important characteristics, this chapter presents the architecture of VCN. It is followed by the detailed analysis of the threats to which each tier-cloud of VCN is vulnerable.

Vehicular Ad-hoc networks (VANETs) have been around for some time and are still attracting research. VANETs are one of the ad hoc networks real-life applications, where vehicles communicate which each other and with fixed components known as roadside units. VANETs have unique characteristics and requirements which differ from those in standard ad-hoc networks, but the security remains a major challenge because of the very dynamic topology and the lack of infrastructure. In this autonomous and auto-organized environment, the question of whom to trust and for what becomes very important and difficult to answer. This paper addresses the issues associated with establishing trust between peers in VANET. It defines, discusses and evaluates various trust management approaches and how well they address VANET requirements. The paper also proposes criteria for assessing the effectiveness of trust management models with regards to challenges specific to VANETs.