Nodes In Vehicular Ad Hoc Networks Research Articles

An optimized secure dual authentication for VANETs

“Vehicular Ad-hoc Network (VANET)” has emerged as a hotbed of research and development. This technology has been utilized to improve vehicle and road safety by transmitting information about traffic congestion and road conditions during emergencies in order to soothe drivers and passengers. However, such environments are more vulnerable to security risks. Thus, the primary challenge in such an environment is to provide authentication services while maintaining user confidentiality. To that end, a trustworthy authority (TA) is established, which offers services to users as well as authentication of communications transmitted between trusted authorities and VANET nodes. A trusted authority (TA) is established using VANETs to supply clients with a range of online premium services. As a result, preserving the confidentiality and authenticity of messages conducted between the TA and VANET nodes is crucial. Our proposed method minimizes recognition latency by up to 85% when compared to On-site Driver ID, and decreases authentication latency by up to 94%, respectively.

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

Event Message Clustering Algorithm for Selection of Majority Message in VANETs

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time.

AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication

ABSTRACT VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput.

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

Hybrid optimization enabled trust-based secure routing with deep learning-based attack detection in VANET

Vehicular ad-hoc network (VANET) model is easy to arrange and inexpensive answer for clever traffic control and traffic failure protective measures. Generally, the nodes present in VANET utilize broadcast protocols for effectively transmitting safety information, although nodes do not perform based on routing protocols. The misbehavior can produce through targeted attack in which aggressive vehicle can purposely send malevolent packets to damage. Moreover, due to the reason of dynamic behavior of nodes in VANET plus routing difficulties, unexpected misconduct also occurs owing to the software or hardware failures in vehicle. The challenges faced by existing techniques are provided here. VANET becomes a most significant concern to enhance the expediency, mobility, and security application in ITS. Precautions are further tough owing to its rapid mobility, wireless communication and large coverage of nodes. Also, the computational time required for executing independent operations are conspicuously maximum when, the number of vehicles is maximized. In order to overcome these challenges, in this paper, hybrid optimization-based Deep Maxout Network (DMN) is developed for attack classification in VANET. The Cluster Head (CH) selection and routing process is performed using designed hybrid optimization algorithm. The feature selection process is more significant in order to perform effective classification process. In addition, the attack classification is carried out using DMN and it is taught by introduced optimization algorithm. The developed optimization-based DMN model obtained better classification performance with precision and recall of 0.9395, and 0.9462 as well as routing performance with energy and trust of 0.2454J, and 0.4402.

On a Blockchain-Based Security Scheme for Defense against Malicious Nodes in Vehicular Ad-Hoc Networks.

Vehicular ad-hoc networks (VANETs) aim to provide a comfortable driving experience. Sharing messages in VANETs can help with traffic management, congestion mitigation, and driving safety. However, forged or false messages may undermine the efficiency of VANETs. In this paper, we propose a security scheme based on blockchain technology, where two types of blockchain are constructed based on roadside units (RSUs) and Certificate Authorities (CAs), respectively. The proposed security scheme has multifold goals to identify malicious nodes and detect forged messages based on multiple factors, such as reputation of sender nodes, and time and distance effectiveness of messages. In addition, an incentive mechanism is introduced on the RSU blockchain to encourage RSUs to adopt active behaviors. Extensive simulations show that the proposed scheme exhibits superior performances to existing methods in detecting forged messages and identifying malicious nodes. Meanwhile, it provides privacy protection and improves the efficiency of vehicular networks.

VANET Clustering-Based Routing Protocol for Vehicular Ad-Hoc Network Using Two Metaheuristic Algorithms

Vehicular ad hoc networks (VANET) are a special model of mobile ad-hoc networks (MANETs)and have many applications in safe driving, and entertainment by exchanging information between vehicles (e.g. road conditions, weather, congestion, etc.). the problem is that these Nodes(vehicles) in VANETs have very high mobility and due to this dynamic nature and the instability of wireless links between vehicles, providing an efficient and reliable routing method is a serious challenge for VANETs. Therefore, in order to meet this challenge and maintain the necessary information exchange between vehicles without loss, deletion, or access late, it is necessary to solve this problem by (designing an efficient and secure routing protocol for the exchange of important data in VANET). In this paper two algorithms were used together (Harris Hawks optimization algorithm (HHO) and Artificial Bee Colony algorithm (ABC)), taking into account the best parameters such as (the amount of free buffer size, distance between nodes, speed of nodes, node degree, and the quality of link connection) to improve the results compared to the rest of the articles and HIR. the simulation results indicated that the methodology improves other algorithms with Relevance in terms of parameters of packet delivery rate, throughput, and end-to-end delay.

VABLOCK: A blockchain-based secure communication in V2V network using icn network support technology

Vehicular Ad hoc Network (VANET) provides efficient communication among vehicles (V2V). The communication among all the vehicles complies with the on-demand, which contains a secure and trustable mechanism to ensure trustable communication. The modification in the communication information may result in falsified information. Secure data is very important in V2V communication to save the lives of pedestrians and drives by delivering secure and accurate information. To address the problem and achieve secure communication in V2V, we proposed a new blockchain-based message dissemination technique to secure V2V communication. With the passive need for adaptable and sufficient content delivery, information-centric networking (ICN) is adopted to enhance trustworthiness communication in VANET. We used Cluster-based secure communication through ICN-based VANETs. As VANET is open, ICN provides direct content requests and responses without location dependency. ICN-based VANET enhances caching abilities. The blockchain-based security protocol is implemented to secure efficient communication without altering the messages. The protocol achieves privacy, security, and trust for detecting malicious nodes in VANET. Additionally, the Clustering technique is applied to adopt in-range communication. Proposed-Caching framework enhances security and provides on-demand data to vehicles. NS-2 simulator is used to simulate the Proposed VABLOCK approach. Experimental results are performed and compared with relevant techniques, which show enhanced results based on cache hit ratio, one hop count, malicious node detection, and delivery ratio. We demonstrate that proposed caching improves results on selected parameters based on results.

An optimized deep learning-based trust mechanism In VANET for selfish node detection

PurposeThis study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.Design/methodology/approachThe authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.FindingsA deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.Practical implicationsThe experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.Originality/valueThis paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

SE-CPPA: A Secure and Efficient Conditional Privacy-Preserving Authentication Scheme in Vehicular Ad-Hoc Networks.

Communications between nodes in Vehicular Ad-Hoc Networks (VANETs) are inherently vulnerable to security attacks, which may mean disruption to the system. Therefore, the security and privacy issues in VANETs are entitled to be the most important. To address these issues, the existing Conditional Privacy-Preserving Authentication (CPPA) schemes based on either public key infrastructure, group signature, or identity have been proposed. However, an attacker could impersonate an authenticated node in these schemes for broadcasting fake messages. Besides, none of these schemes have satisfactorily addressed the performance efficiency related to signing and verifying safety traffic-related messages. For resisting impersonation attacks and achieving better performance efficiency, a Secure and Efficient Conditional Privacy-Preserving Authentication (SE-CPPA) scheme is proposed in this paper. The proposed SE-CPPA scheme is based on the cryptographic hash function and bilinear pair cryptography for the signing and verifying of messages. Through security analysis and comparison, the proposed SE-CPPA scheme can accomplish security goals in terms of formal and informal analysis. More precisely, to resist impersonation attacks, the true identity of the vehicle stored in the tamper-proof device (TPD) is frequently updated, having a short period of validity. Since the MapToPoint hash function and a large number of cryptography operations are not employed, simulation results show that the proposed SE-CPPA scheme outperforms the existing schemes in terms of computation and communication costs. Finally, the proposed SE-CPPA scheme reduces the computation costs of signing the message and verifying the message by 99.95% and 35.93%, respectively. Meanwhile, the proposed SE-CPPA scheme reduces the communication costs of the message size by 27.3%.

A Cross-layer Method for Identifying and Isolating the Blackhole Nodes in Vehicular Ad-hoc Networks

ABSTRACT Vehicular Ad-hoc Network (VANET) is a set of intelligent vehicles that communicate without a fixed infrastructure. The communication between each source/destination pair is done by using routing protocols. On-demand multipath distance vector (AOMDV) is one of the most known ad-hoc multipath routing protocols used in VANETs. The decentralized nature of VANET makes this type of network vulnerable to various attacks, such as blackhole attack. In such attack, the malicious vehicle aims to make the communication unavailable. To achieve this goal, the malicious vehicle persuades the source to send its data packets through it because it has the fresher route toward the destination. This is done by forging routing information. After receiving the data packets, the malicious vehicle deletes them instead of forwarding them to their intended destinations. This paper introduces a new Cross-Layer method (CRAOMDV) where information is shared between MAC and network layers to detect and ignore the malicious vehicles in VANETs. Our experiments used the simulator NS2 and SUMO for the generation and simulation of real mobility scenarios. The evaluation results demonstrate the efficiency of CRAOMDV compared to AOMDV under blackhole attack in terms of improving the packet delivery and reducing the average end-to-delay and the routing overhead.

Deep Q-network based fog node offloading strategy for 5 G vehicular Adhoc Network

The research on the Vehicular ad-hoc network (VANET) has been accelerated by the 5 G technology. The software-defined network and fog nodes near the vehicles have improved the throughput and latency in the processing of requests. However, the fog nodes are limited with computational resources like memories, RAM, etc. and need to be optimally managed. The estimation of vehicles' future locations can help in the optimal offloading of vehicles' processing requests. This paper has introduced the Kalman filter prediction scheme to estimate the vehicle's next location so that the future availability of fog resources can help in the offloading decision. The deep Q network-based reinforcement learning is used to select the resources-rich fog node in VANET. The Long Term Short Memory-based Deep Q-Network optimally offloads the tasks of the fog nodes as per their available resources thus giving much better performance. The proposed Deep Q-Network algorithm is an efficient solution to offload the request optimally which improves the overall performance of the network. It is found that the average reward by proposed Deep Q-Network is 56.889% more than SARSA learning and is 44.727% more than Q learning.

Enhancing Mobility Based on Realistic Models for Vehicular Ad Hoc Networks

Creation and maintaining of the one-to-one communication link between the nodes in Vehicular Adhoc Networks are challenging. The model which is very much efficient for one particular scenario will not work at the same level of efficiency for another environment. The mobility of the nodes in the network plays a crucial role in establishing a reliable communication model in the VANETs. Analyzing the nature of mobility inside a particular network based on logical and historical data paves an efficient way in the routing of packets by predicting the best route and improve the quality of the network, reliability, and other performance in terms of serviceability. This paper aims to analyze the drawbacks of existing mobility models utilizing various network quality parameters by classifying them into microscopic and macroscopic mobility models. With the insight gained from the analysis, we propose two methodologies where the realistic model for the VANETs can be established. The models are architected with the help of the information provided by the Geographic information system. The conventional mobility models include excessive details such as road and street layouts, intersection with traffic signals, acceleration and deceleration, building, and other obstacles in a realistic mobility model, that requires prolonged time to design and optimize, it should complicate the simulation. Designing an effective, realistic mobility model is crucial. The key objectives of this proposal are to architect realistic VANETs mobility models by taking into account the real-time road environment and actual data according to the traffic demand and improving the real-time performance of VANETs

Protocol misbehavior detection framework using machine learning classification in vehicular Ad Hoc networks

A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy. Nodes in Vehicular Ad Hoc Networks (VANETs) use broadcast protocols to efficiently disseminate safety information, but nodes do not always behave according to the routing protocols. Misbehavior can be caused by a targeted attack, where an attacking vehicle can intentionally send or route malicious packets to harm. Due to the dynamic nature of nodes in VANETs and routing complexity, unintentional misbehavior can also happen due to hardware or software failures in the vehicle. We are not concerned with the targeted attacks, but rather explore how the unintentional misbehavior, which can cause statistical multi-hop routing protocols to operate as basic flooding protocols, can be detected and accurately classified. These methods and detection techniques are based on the IEEE 802.11p MAC layer and weighted p-persistence multi-hop routing protocol. The linear classification was done using the TensorFlow framework and evaluations were performed using the VEINS simulator using the p-persistence broadcast protocol in a US city area.

Privacy-preserving batch verification signature scheme based on blockchain for Vehicular Ad-Hoc Networks

Along with the development of Internet of Things (IoT) technology and Vehicular Ad-Hoc Networks (VANETs), security and effectiveness of road utilization have also been greatly improved. Currently, many certificateless signature schemes have been proposed to improve the communication speed while protecting the communication contents and real identities of the vehicles. Among others, some schemes realize batch verification or aggregation of multiple signatures. However, they either incur high calculation costs or cannot execute the operation of batch verification successfully. In this paper, we propose an efficient and privacy-preserving certificateless public key signature scheme based on the blockchain. Firstly, our scheme provides support for batch verification and aggregation of the signatures, and has lower calculation cost compared with the previous ones. Secondly, our scheme adds two blockchains to the structure in order to protect the identity privacy of the vehicles. Utilizing the immutability and openness of the blockchain, all nodes in VANETs can verify whether the identity of the vehicle is legal. Therefore, the scheme can greatly reduce the risk of privacy disclosure, and the pseudo-identities can be generated by the vehicles themselves. Thirdly, our scheme can realize traceability of the vehicles, which means the real identities of the vehicles can be revealed if they commit an illegal behavior. The experimental results show that our scheme can greatly reduce the computational cost of the vehicles and provide the functions of batch verification and aggregation. Therefore, the proposed scheme can simultaneously improve security and efficiency of the signature schemes for VANETs.

АНАЛИЗ ПРОИЗВОДИТЕЛЬНОСТИ ПРОТОКОЛОВ РЕАКТИВНОЙ МАРШРУТИЗАЦИИ В VANET НА БАЗЕ NS3

Since importance of improving of Intelligent Transportation System (ITS) always follow modern trends by using new wireless communication technologies, the trend of latest research topics is focusing on Vehicular Ad Hoc Network (VANET). VANET networks play a vital role in ITS due to their increasing importance for the building of ITS. VANET is a subclass of mobile ad-hoc networks (MANET). VANET depends on wireless technologies to establish communication between moving vehicles (nodes). An appropriate and efficient routing protocol helps to successful exchange data between mobility nodes in vehicular ad-hoc networks. VANET has a lot of similar features to MANETs such as finite bandwidth, self-arrangement, self-administration, and unstable network topology. Except it has some important features of its characteristic such as very high node mobility, delay restrictions, and frequent network outages. For this reason, routing in VANET networks is much more complex than routing in MANET networks. The purpose of this study - to evaluate the performance of protocols AODV (Ad hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing) and their impact on the performance of networks VANET. This paper differs in that it analyses the impact of network size at a large number of nodes, and different vehicle speeds on network performance metrics like packet delivery ratio, throughput, average delay, overhead and packet loss ratio and assessing the level of network performance at realistic mobility scenarios for the movement of vehicles in the street generated by Bonnmotion tool. Also, the simulation is carried out in NS-3 simulator to create VANET network topology and routing protocols.

Research on Data Transmission Optimization Of Communication Network Based on Reliability Analysis

This paper briefly introduced the vehicular communication network, vehicular ad-hoc network (VANET), and the ant colony algorithm for path search in the network, improved the ant colony algorithm, carried out a simulation experiment on the improved ant colony algorithm on the simulation platform, and compared it with the traditional ant colony algorithm and the geographical location-based routing algorithm. The results showed that the network packet loss rate and time delay showed a tendency of increasing first and then decreasing under the three algorithms with the increase of vehicle nodes in VANET; with the increase of nodes, the network reliability under the improved ant colony algorithm showed a tendency of increasing first and then being stable, and the network reliability under the other two algorithms showed a tendency of increasing first and then decreasing; under the same number of nodes, the packet loss rate and average delay were lower, and the reliability was higher under the improved ant colony algorithm.

Mobility Prediction issues and Challenges in Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are gaining much attention from both industry and academia communities as an emerging technology. VANET is composed of self-regulating nodes where the vibrant node mobility changes the network topology frequently. The important aim of VANET is to upgrade road safety conditions to minimize extent of road mishaps. The nodes are very mobile in vehicular ad hoc network so routing becomes complex job. As the VANET nodes move very fast the topology changes accordingly. So node mobility prediction in VANET is a very important issue. Further, prediction of mobility helps to estimate the stable path between the nodes which leads to better routing. Estimating secure paths among the routing of nodes perform in a better way, thereby reducing the overhead and minimizing interruptions in connections. This paper explores VANET's basic architecture and discusses a number of current mobility prediction techniques, and concludes with performance analysis of existing routing protocols and proposed mobility prediction methods.

Review of Prevention Schemes for Man-In-The-Middle (MITM) Attack in Vehicular Ad hoc Networks

Vehicular Ad-Hoc Network (VANET) is an indispensable part of the Intelligent Transportation System (ITS) due to its abilities to enhance traffic management and safety. Many researchers have been focused on specific areas involving management and storage data, protocols standardization, network fragmentation, monitoring, and quality of service. The benchmarks of security of VANET are studied and figured out in this paper. VANET provides the driver and passenger with the safety application as well as entertainment service. However, the communication between nodes in VANET is susceptible to security threats in both communication modes, which indicates the main hazard. In this paper, we identified different Man-In-The-Middle (MITM) attacks with various behaviors such as message tampering, message delaying, and message dropping, according to the literature. In this study, the essential background of VANET from architectural point of view and communication types are discussed. Then, the overview of MITM attack in VANET is presented. In addition, this paper thoroughly reviews the existing prevention schemes for MITM attack in VANET. This review paper reveals that there is still a need for a better and more efficient preventive scheme to address the MITM attack in VANET. This review paper could serve as evidence and reference in the development of any new security schemes for VANETs.