Attacks In Vehicular Ad Hoc Networks Research Articles

Zone-based stable and secure clustering technique for VANETs

Vehicular Ad-hoc Network (VANET) is a pivotal element of the Intelligent Transportation System (ITS) and has gained significant attention in both academia as well as in the industry. The driving experience has been improved with the deployment of VANETs in the transportation system. In this scenario, accurate real-time traffic information is required to be forwarded to the correlated vehicles using Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The V2V system is prone to the issue of communication overhead and security, whereas the V2I-based model for data dissemination faces the issue of scalability. In addition to this, wireless communication among vehicles is affected by potential attackers, that not only threaten the vehicle's security but also the commuter's life. An impersonation attack is considered the most common and dangerous attack in VANETs. In this paper, we propose a hybrid system that utilizes both V2V and V2I communications for secure data dissemination in urban scenarios. We use a zone-based stable and secure clustering mechanism to identify Cluster Heads (CH) as a message forwarding node to and from the Road Side Unit (RSU), which reduces the communication overhead and latency of the network. We utilize the concept of Internet Control Message Protocol (ICMP) and the monitoring mode of RSU for the detection and mitigation of impersonation attacks in VANETs. Finally, we validate our results using a network simulator (NS2).

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.

Unlinkable Signcryption Scheme for Multi-Receiver in VANETs

An increasing number of researchers are turning their attention to signcryption, particularly in the context of multi-receiver communication scenarios, due to its ability to simultaneously provide authentication, integrity, and confidentiality of messages. However, existing signcryption schemes have not been able to fully implement sender unlinkability. Specifically, when a sender signcrypts a secret message and obtains the corresponding ciphertext, the intended recipient must use the sender’s identity or public key to complete the unsigncryption process and retrieve the plaintext. Consequently, the recipient can link the sender via the same identity or public key. To address this issue, we present an Unlinkable Signcryption Scheme for Multi-Receiver (USS-MR). With Chinese Remainder Theorem (CRT), our USS-MR enables a vehicle to send the same secret message to a group of RoadSide Units (RSUs). Additionally, when a new message requires signcryption, the vehicle generates a new key pair, making it impossible for any RSU to link the vehicle through its public key. In our USS-MR, we have adopted a pseudonym mechanism to provide conditional privacy, which hides the real identity of the vehicle through the use of pseudonyms and avoids linking it to the identity. Moreover, if a vehicle is found to engage in malicious behavior, it will not only be tracked but also subjected to revocation. Comprehensive security analyses demonstrate that our USS-MR satisfies various security, privacy, and functionality requirements and effectively resists common attacks in Vehicular Ad-hoc Networks (VANETs). Finally, our USS-MR demonstrates certain advantages in terms of computation and communication when compared to relevant studies. In particular, our USS-MR maintains a consistent communication burden of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$388$</tex-math> </inline-formula> bytes.

Analysis and Improvement of an Efficient Certificateless Aggregate Signature With Conditional Privacy Preservation in VANETs

Certificateless aggregate signature reduces the trust in key generation center and eases the inconvenience of certificate management at a low cost of communication and computation. Because of these merits, a lot of new certificateless aggregate signature schemes have been proposed to provide enough security and privacy in vehicular ad hoc networks (VANETs). However, many of these schemes are insecure against various attacks. In this article, we analyze the security of an efficient certificateless aggregate signature with conditional privacy preservation in Internet of vehicles. The results of the analysis show that it is insecure against forgery attack. In order to overcome the security vulnerability, we put forth an improved scheme. Then, the formal and informal security analysis of our improved scheme both are provided, which show that the improved scheme meets the expected security requirements and is secure against several known attacks in VANETs. Finally, performance evaluation shows that our improved scheme is feasible for the VANETs in terms of computation and communication overhead.

MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs

Sybil attacks in Vehicular Ad-Hoc Networks (VANETs) conduct malicious behavior by falsifying and faking messages between vehicles. It poses a significant threat to the safety of vehicle movement. Meanwhile, because Sybil attacks often hide the real identity of the attacker with the help of a legitimate pseudonym, making it very difficult to detect them. Most existing detection schemes use a single data source, which is not enough to describe the specific characteristics of the attack behavior accurately, while their detection performance is also affected by real scenario factors such as traffic flow and attacker density. Therefore, we propose a multi-source data fusion detection framework for Sybil attacks based on the study of the behavior characteristics of Sybil attacks and the impact of the attacks on the traffic flow state. We get basic safety messages data, map data and sensor data and then obtain multi-dimensional data fusion features from four aspects: spatio-temporal location relationship, traffic flow state change, vehicle behavior characteristics and sensor data verification, and finally use machine learning classification model to complete the detection of attack behavior. Experimental results show that our proposed attack detection framework is able to locate the specific road section where the attack occurred in a realistic and complex traffic scenario containing different road types without using trusted vehicles as observation nodes, and has good generalization capability. the average detection accuracy of the MDFD framework for four types of compound attacks is as high as 97.69%.

Detection method to eliminate Sybil attacks in Vehicular Ad-hoc Networks

A Sybil attack is caused by a malicious vehicle node stealing fake identities and continuously generating fake vehicles on the road to create the illusion of congestion, which endangers normal vehicles on the road. Because Sybil vehicle nodes have trajectories and motion states similar to those of normal vehicles, they are more difficult to detect in high-density traffic environments. The real-time authentication of vehicles is impossible in the existing traffic environment; thus, malicious vehicle nodes with a high degree of stealth can continuously attack and are difficult to stop. In this paper, we propose a Sybil attack detection method based on basic security message (BSM) packets, which exploits the characteristic that BSM packets have a unique sending source and uses the spatiotemporal relationship of dynamic vehicle location changes to detect and trace Sybil attacks. A weighted integration strategy is proposed for increasing the detection precision without machine-learning model prediction. Experimental results indicated that the proposed method can detect Sybil attacks in real time and is not affected by the attack density or traffic density. Moreover, it can detect Sybil nodes and trace malicious nodes simultaneously, with precisions of ¿98% and ¿94%, respectively, resolving the difficulties of existing detection schemes.

Lightweight and Secure Multi-Factor Authentication Scheme in VANETs

The idea of a smart city and the utilization of vehicles are emerging rapidly. For inter-vehicular communication, this progression builds Vehicular Ad-Hoc Network (VANET) an extensively used network. The VANET is used to obtain information regarding the road's condition, location of vehicles, speed, and traffic congestion description. The exchange of information in VANET is public which leads to severe security threats. Generally, one of the most crucial tasks in VANET is the security of data. So it becomes one of the urgent tasks of researchers. We have reviewed several authentication schemes for VANETs. Still, most schemes are not secure due to several attacks such as replay, denial of services, impersonation attacks, etc. Furthermore, the existing schemes used higher communication and computational costs, and they are also incompatible with VANET applications that are resource-constrained. This article presents the lightweight and secure multi-factor authenticated key agreement scheme for VANETs to ensure efficient and secure transmission of data via an open channel. Our presented scheme is secure, scalable, and lightweight compared to existing schemes due to its low computation and communication cost. The formal and informal security analysis proved that our scheme is more reliable, practical, and secure against several unknown adversarial attacks in VANETs.

Toward Attack-Resistant Route Mutation for VANETs: An Online and Adaptive Multiagent Reinforcement Learning Approach

Vehicular Ad hoc Networks (VANETs) are prone to packet drop attacks because of their inherent distributed architecture and dynamic topology. Existing security schemes mainly focus on multi-path and trust-based routing. Unfortunately, the former causes high energy consumption and the latter requires trust assessment, which is not easy to implement in practice. Route mutation (RM) is emerging as an active defense technology that changes routes periodically. Traditional RM is conceived for fixed network topologies, and needs a centralized controller, so that it cannot be applied to VANETs. Therefore, the present contribution investigates RM in VANETs by proposing a Grid-based extended Joint Action Learning approach (Grid-eJAL). To the best of our knowledge, this is the first contribution that designs an online and adaptive multi-agent reinforcement learning (MARL) for RM to mitigate attacks in VANETs. Differently from existing MARL schemes, Grid-eJAL allows vehicles to share parameters to accelerate the convergence speed of learning. In Grid-eJAL, the area of interest is split in equally sized grids and, when a vehicle transmits packets, the next hop with the minimum angle of mobility is selected within the grid considered as optimal by the learning policy. The convergence of Grid-eJAL is proved theoretically. Finally, extensive simulation results highlight the effectiveness of Grid-eJAL compared to representative state-of-the-art solutions.

On trust management in vehicular ad hoc networks: A comprehensive review

Security issues have always posed a major threat and challenge to the Internet of Things (IoTs), especially the vehicular ad-hoc networks (VANETs), a subcategory of IoTs in the automotive field. The traditional methods to solve these ever-growing security issues in VANETs are mainly cryptography-based. As an effective and efficient complement to those solutions, trust management solutions and reputation models have been widely explored to deal with malicious or selfish vehicle intrusion and forged data spoofing, with the aim of enhancing the overall security, reliability, trustworthiness, and impartiality of VANETs. For the integrity of the article, this survey begins with providing the background information of VANETs, including the basic components and general architecture. Then, many attacks in VANETs are investigated, analyzed, and compared to understand the functional relevance of the following trust and reputation methods. Various approaches offer various countermeasures against these types of attacks. At the same time, the latest development of emerging technologies such as blockchain, software-defined network, and cloud computing opens up new possibilities for more and more promising trust and reputation management models and systems in VANETs. After that, the survey reviews the most important trust and reputation models and schemes which are widely mentioned in the literature based on our developed technique-based taxonomy, in contrast to the popular “entity-centric, data-centric, hybrid” taxonomy in the field, to adapt to the recent technological development of these management schemes in VANETs. Finally, discussions and speculations on the future direction of research into the trust and reputation management in VANETs are presented.

Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches

Detecting the attacks in Vehicular Ad hoc Network (VANET) system is very important to provide more secure and reliable communication between all vehicles in the system. In this article, an effective Intelligent Intrusion Detection System (IDS) is proposed using machine learning and deep learning approaches such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Convolutional Neural Networks (CNN), respectively. The existing methods focus on detecting only the known attacks in VANET environment. This limitation is overcome by proposing the Intelligent IDS system using soft computing techniques. The proposed method consists of Known IDS (KIDS) and Unknown IDS (UIDS) modules, which detect both known attacks and unknown attacks. The KIDS module uses ANFIS classification module to detect the known malicious attacks, whereas the UIDS module uses a deep learning algorithm to detect the unknown attacks in VANET. Modified LeeNET (MLNET) architecture is proposed in this article to identify the type of unknown attacks. In this work, DoS attacks, Botnet attacks, PortScan attacks, and Brute Force attacks are detected using this hybrid learning algorithm. The proposed system obtains 96.9% of Pr, 98.3% of Se, 98.7% of Sp, and 98.6% of Acc and consumed 1.75 s for detecting the DoS attack on i-VANET dataset. The proposed system obtains 98.1% of Pr, 98.9% of Se, 98.1% of Sp, and 98.1% of Acc and consumed 0.95 s for detecting the Botnet attack. The proposed system obtains 98.7% of Pr, 99.1% of Se, 98.9% of Sp, and 99.2% of Acc and consumed 1.38 s for detecting the PortScan attack. The proposed system obtains 99.1of Pr, 97.8% of Se, 98.7% of Sp, and 98.5% of Acc and consumed 1.29 s for detecting the Brute Force attack. The developed methodology is tested on the real-time CIC-IDS 2017 dataset, and the experimental results are compared with other state-of-the-art methods.

Sybil Attack Detection in VANET Using Machine Learning Approach

VANET (Vehicular Ad-hoc Network) is a subclass of MANET in which many cars can connect with one another via node to node or equipment erected on the side of the road. However, due to the adaptability of centres and the unexpected trade in geography, there may be opportunities for attacks in VANET. One of the ostensible assaults is the Sybil attack, in which the attacker fabricates unequivocally unique equal personalities to undermine the value of VANET. Sybil creates fictitious identities inside the community as well in order to sabotage attempts to mediate conversations between community nodes. Sybil assaults have an impact on carrier transportation in relation to things like traffic congestion, road safety, and multimedia entertainment. VANETs therefore announce a security mechanism to protect you from Sybil attacks. In this regard, this work puts forth the SDTC method, which completely relies on machine learning techniques to prevent Sybil assaults in VANETs. In order to reduce identification time, increase detection accuracy, and enhance scalability, the SDTC (Sybil node detecting the use of Classification) mechanism uses a few vehicle-specific Extreme Learning Machine (ELM) features. The results suggest that SDTC is a suitable strategy to reduce Sybil assaults and sustain provider service in VANETs.

A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs

Routing security attacks in Vehicular Ad hoc Networks (VANETs) represent a challenging issue that may dramatically decrease the network performances and even cause hazardous damage in both lives and equipment. This study proposes a new approach named Multivariate Statistical Detection Scheme (MVSDS), capable of detecting routing security attacks in VANETs based on statistical techniques, namely the multivariate normality tests (MVN). Our detection approach consists of four main stages: first, we construct the input data by monitoring the network traffic in real time based on multiple metrics such as throughput, dropped packets ratio, and overhead traffic ratio. Secondly, we normalize the collected data by applying three different rescaling techniques, namely the Z-Score Normalization (ZSN), the Min-Max Normalization (MMN), and the Normalization by Decimal Scaling (NDS). The resulting data are modeled by a multivariate dataset sampled at different times used as an input by the detection step. The next step allows separating legitimate behavior from malicious one by continuously verifying the conformity of the dataset to the multivariate normality assumption by applying the Rao–Ali test combined with the Ryan–Joiner test. At the end of this step, the Ryan–Joiner correlation coefficient (R–J) is computed at various time windows. The measurement of this coefficient will allow identifying an attacker’s presence whenever this coefficient falls below a threshold corresponding to the normal critical values. Realistic VANET scenarios are simulated using SUMO (Simulation of Urban Mobility) and NS-3 (network simulator). Our approach implemented in the Matlab environment offers a real time detection scheme that can identify anomalous behavior relying on multivariate data. The proposed scheme is validated in different scenarios under routing attacks, mainly the black hole attack. As far as we know, our proposed approach unprecedentedly employed multivariate normality tests to attack detection in VANETs. It can further be applied to any VANET routing protocol without making any additional changes in the routing algorithm.

Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal fuzzy support vector machine (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.

Secure VANET Authentication Protocol (SVAP) Using Chebyshev Chaotic Maps for Emergency Conditions

It is crucial to support emergency applications provided by vehicular adhoc network (VANET) through enabling vehicles to quickly access to the infrastructure and consequently request rescue services. Additionally, the communication channels between vehicles and the infrastructure lack various security features due to the inferior wireless characteristics of their environment. However, most of the existing authentication schemes which are used to fix the security drawbacks lead to heavy computations and large storage burdens on the vehicle onboard unit (OBU). These schemes utilize secure channels while distributing the network parameters between the various participants. Yet, it is not efficient to establish secure channels during the interactions between entities. Furthermore, lightweight cryptography is an efficient security solution which is adequate for OBU to maintain a reasonable efficiency with low computational and communication costs. Two basic demerits for lightweight authentication protocols are highlighted as follows: Firstly, symmetric key-based authentication protocols dismiss achieving non-repudiation feature, leading to several security attacks in VANET. Secondly, public key-based authentication schemes are relied on elliptic curve cryptography (ECC) which makes the protocol implementation more difficult. Hence, this paper introduces a novel authentication protocol that utilizes Chebyshev chaotic maps to secure connectivity between the vehicles and infrastructure without using secure channels to distribute the network parameters. The new protocol combines the concept of the symmetric key cryptography with the public key signature to satisfy both the lightweight property and non-repudiation feature. Thus, this protocol introduces a novel network model which is the lowest hardware complexity, compared with others. The performance analysis is performed by Wolfram Mathematica, proving that the proposed protocol is superior in terms of security and performance aspects; its computation and storage costs of OBU are enhanced with 24.09&#x0025; and 16.99&#x0025;, respectively, compared to the most competing scheme. Besides, the Scyther simulation confirms the security of the protocol.

Detection Method to Eliminate Sybil Attacks in Vehicular Ad-Hoc Networks

Detection Method to Eliminate Sybil Attacks in Vehicular Ad-Hoc Networks

Investigation of the effectiveness of metric classification methods in identifying attacks in VANET

This paper discusses the problem of improving the efficiency of metric machine learning methods of identification attacks in vehicular adhoc networks (VANETs). The main idea of this research is to select the type of nonlinear functions for calculating the distances between the objects of the sample, describing the traffic of VANET using metric methods, such as the method of k-nearest neighbour with linearly decreasing weights and the Parzen window method. The analysis of the effectiveness of the methods considered was carried out on a synthetically generated sample with three different types of attacks on the network. Computational experiments have shown that the k-nearest neighbour method with decreasing weights based on an exponential function with base a < 1 is more efficient than the Parzen window method by about 0.3% and has an accuracy of 84.15%.

Sybil Attack with RSU Detection and Location Privacy in Urban VANETs: An Efficient EPORP Technique

In recent years, Vehicular ad hoc networks (VANETs) could facilitate the decision-making progress of the drivers for example trip planning with the consideration of traffic. In the VANET, the Sybil attack is a very serious attack that collapses the security. In literature, some of the methods are reviewed to detect Sybil attacks in VANETs, but it fails to achieve Sybil attack detection. Hence, in this paper, Emperor Penguin Optimization-based Routing protocol (EPORP) is developed for detecting the Sybil attack which enhances the VANETs security. The main motive of the research is detecting the Sybil attack in VANETs for enhancing the secure operation. In the proposed approach, the Sybil attack will be detected with the help of the Rumour riding technique. To enhance the security of the VANETs, the Split XOR (SXOR) operation is utilized. In the SXOR operation, the optimal key is selected with the help of Emperor Penguin Optimization (EPO). The proposed method is implemented in the NS2 platform and performances are evaluated by metrics such as delay, throughput, delay, encryption time, and decryption time. The proposed method is compared with existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) respectively. While analyzing the delivery ratio, the proposed method has 0.96 s, and the WOA, PSO, and FA are 0.94, 0.92, and 0.90 respectively. From the analysis, the proposed method has a high delivery ratio value compared with the WOA, PSO, and FA methods. Similarly, the other parameters are analyzed and compared with the existing methods.

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial-of-service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software-Defined Networking (SDN) controller and Neural Network Self-Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.

A Privacy-Preservation Framework Based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET

In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

A Survey on Privacy-Preserving Authentication Schemes in VANETs: Attacks, Challenges and Open Issues

Vehicular ad hoc Networks (VANETs) are an emerging technology with robust applications in Intelligent Transport System. It consists of smart vehicles and roadside infrastructure which communicate through open access wireless networks. The rapid growth in vehicles results in VANETs becoming large-scale, dynamic, heterogeneous and it is possible for the attacker to harm vehicular communication which leads to life-endangering situations. VANETs must ensure secured vehicular communication using strong privacy-preserving and authentication mechanisms. In addition, efficiency is also a major concern in VANETs. Numerous studies have been discussed in literature for VANETs privacy and security. Nevertheless, no one covered the privacy and security issues as a holistic view. In this paper, we have given a detailed background overview of VANETs. Details of different possible attacks in VANET are also given in this paper. We have classified privacy and authentication schemes into four major groups with their security mechanisms, security requirements, strength, limitations, attacks countermeasures and performance measures. Finally, we have discussed some open issues in the field of VANETs security.