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. 

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

Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different strategies for detecting various forms of network attacks. However, VANET is still exposed to several attacks, specifically Sybil attack. Sybil Attack is one of the most challenging attacks in VANETS, which forge false identities in the network to undermine communication between network nodes. This attack highly impacts transportation safety services and may create traffic congestion. In this regard, a novel collaborative framework based on majority voting is proposed to detect the Sybil attack in the network. The framework works by ensembling individual classifiers, i.e., K-Nearest Neighbor, Naïve Bayes, Decision Tree, SVM, and Logistic Regression in a parallel manner. The Majority Voting (Hard and Soft) mechanism is adopted for a final prediction. A comparison is made between Majority Voting Hard and soft to choose the best approach. With the proposed approach, 95% accuracy is achieved. The proposed framework is also evaluated using the Receiver operating characteristics curve (ROC-curve).

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Collaborative Learning Based Sybil Attack Detection in Vehicular AD-HOC Networks (VANETS)

Vehicular ad hoc networks (VANETs) connect two or more vehicles wirelessly to enable data exchange in an Internet of Things (IoT) environment. In VANETs, location privacy is the most crucial piece of information, and its protection is the top priority. However, the location privacy threats have not been adequately addressed in positioning for IoT in VANETs. This paper provides an overview of location privacy attacks and their solutions to address the problems caused by attacks in any IoT environment. Secondly, we have analyzed specific solutions based on anonymity (pseudonym) and cryptographic solutions using a digital signature technique. This enables to improve user privacy and security of location-based services for IoT in VANET. Moreover, we have proposed a faster 5G solution for the VANETs as it rapidly disseminates the data in fast-moving vehicles. 

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

VANETs are a kind of mobile ad hoc network (MANET) that provides a wide range of safety applications to vehicle passengers. VANETs are gaining popularity as a measure of the capacity to connect to a diverse set of unseen services. VANETs also enable road operators to monitor and monitor cars in order to reduce risky driving and give prompt relief. However, these networks create design, routing, communication, and security challenges. These networks are subject to a number of security vulnerabilities due to the utilization of wireless communication channels. When a malicious vehicle or RSU can assume several identities, it offers one of the most serious threats to the survival of such networks. VANET is susceptible to a range of assaults because to its lack of infrastructure. The Sybil assault is one of them. A malevolent vehicle in a V ANET may transmit false messages about traffic, an accident ahead, a road restriction, and so on. This may force the driver to take a diversion, placing him in danger of an unfavorable incident. V ANET has shown a variety of issues. The security service technologies that are available, as well as the corresponding processing algorithms. This article discusses the problems of the Sybil attack in VANE T technology. Also, this research will investigate and discuss recently proposed approaches for the Sybil attack.

Effect Sybil attack on security Authentication Service in VANET

Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm

Vehicular ad-hoc networks (VANET) technology, which is an open-access network, renders a quick, simple to deploy, and cheap solution for intelligent traffic control as well as traffic disaster preventive measure but it is prone to disparate sorts of attacks. The Sybil attack (SA) is the most harmful attacks that the VANET has to face. In this, the attacker generates manifold identities to fake manifold nodes. It is extremely onerous to defend as well as detect, especially if it is commenced by means of some connived attackers utilizing their genuine identities. Here, a deep learning-centered intrusion detections system (IDS) is proposed utilizing CMEHA-DNN for detecting the SA in VANET. The proposed technique encompasses ‘4’ steps: (i) cluster formation (CF), (ii) cluster head (CH) selection, (iii) attack detection, and (iv) security of VANET. Initially, the MKHM clustering algorithm clusters the vehicles. Next, the Floyd–Warshall algorithm (FWA) selects the CH as of the clusters. Subsequent to CH selection, the malevolent CH is identified utilizing the deep leaning model CMEHA-DNN by means of extracting the pertinent features as of the CH. Lastly, subsequent to detection, if the CH is a normal one, the information contained by means of the CH is securely sent to the cloud utilizing the MD5-ECC. The proposed work attains an accuracy of 98.37% and a security level of 98.2%, which is better compared to existing methods.

Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC

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.

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

Networking technology in the present scenario has the latest invention called ad hoc networks. VANET is a type of ad hoc network where vehicles communicate with each other in a network. From the last few decades, VANET is becoming popular where relocation, mobility, and converge of location by the vehicles were impossible to achieve by using a wired network. VANET provides the growth of intelligent transport systems by providing safety and comfort consideration for both drivers and passengers. VANET is an open-access network that may prone to various types of attacks. This paper is an overview of the study of various attacks in VANET focusing on Sybil attack in VANET which operates under the urban environment. A comparison of different methods of detection technique for Sybil attack is also included.

A. Anitha

Papers

Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC

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

Sybil Attack in VANET Operating in an Urban Environment: An Overview