Attacks In Vehicular Ad Hoc Networks Research Articles

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.

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.

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 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.

Evaluating the Impact of DDoS Attacks in Vehicular Ad-Hoc Networks

The presence of either malicious vehicles or inaccessibility of network services makes vehicular ad-hoc networks (VANETs) easy targets for denial of service (DoS) attacks. The sole purpose of DoS attacks is to prevent the intended users from accessing the available resources and services. When the DoS attack is carried out by multiple vehicles distributed throughout the network, it is referred as distributed DoS (DDoS) attack. A lot of works have been done by researchers in securing the vehicular communication against the DDoS attacks. In this paper, efforts have been made to simulate the DDoS attacks in VANETs as well as to study their impact on the performance of networks. The experimental results are presented using six important metrics, which are collision, jitter, delay, packet drop, ratio-in-out, and throughput. The experimental results show that the impact of distributed DoS (DDoS) attack on networks performance is very critical and must be addressed to ensure the smooth functioning of networks.

Hybrid Algorithm to Detect DDoS Attacks in VANETs

Security and safety are fundamental issues in any wireless network. The problem becomes serious when the specified network is Vehicular Adhoc Network (VANET). VANET faces Distributed Denial of Service (DDoS) attacks, when several vehicles carry out various types of Denial of Service (DoS) attacks to disrupt the normal functioning of network, thereby endangering human lives. A highly efficient and reliable algorithm is required to be developed to detect and prevent DDoS attacks in VANET. This paper presents a hybrid detection algorithm based on the SVM kernel methods of AnovaDot and RBFDot for detecting DDoS attacks in VANETs. In this hybrid algorithm, features like collisions, packet drop, jitter etc. have been used to simulate real time network communication scenario where the network is operating under normal conditions, as well as under DDoS attacks. These features are used both for training and for testing the model based on the proposed hybrid algorithm. The performance of the model based on the proposed hybrid algorithm is compared with the models based on single SVM kernel algorithms AnovaDot and RBFDot based on Accuracy, Gini, KS, MER and H. The experimental results show that the model based on the proposed hybrid algorithm is superior to detect DDoS attacks as compared to the models based on single SVM kernel algorithms AnovaDot and RBFDot. The results also prove that by combining the the SVM kernel algorithms, an efficient and effective hybrid algorithm can be developed.

Decision Tree and Neural Network Based Hybrid Algorithm for Detecting and Preventing Ddos Attacks in VANETS

The demand of Vehicular Adhoc Networks (VANETs) has been increasing in the area of vehicular and infrastructure communications. It has been felt that there is requirement of sharing of critical information related to safety and traffic management among different types of vehicles in a secure way. To ensure the smooth operation of the network, the availability of network resources is needed. The presence of either malicious vehicles or inaccessibility of network services makes VANET easy target for denial of service (DoS) attacks. The sole purpose of DoS attacks is to prevent the intended users from accessing the available resources and services. When the DoS attack is carried out by multiple vehicles distributed throughout the network, it is referred as Distributed DoS (DDoS) attack. The DDoS attacks are very dangerous and hard to be addressed in real time. The machine learning based DDoS attack detection algorithms have been proposed and presented by the research community in literature. In this paper, a hybrid algorithm of Decision Tree and Neural Network is presented for detecting and preventing different types of DDoS attacks in VANETs with highly efficient results. The simulation based experiments are carried out in order to evaluate and compare the performance of proposed hybrid algorithm with respect to different performance parameters. Based on experiments results, it has been found that the performance of hybrid algorithm has been increased significantly.

A Survey of Security Services, Attacks, and Applications for Vehicular Ad Hoc Networks (VANETs).

Vehicular ad hoc networks (VANETs) are an emerging type of mobile ad hoc networks (MANETs) with robust applications in intelligent traffic management systems. VANET has drawn significant attention from the wireless communication research community and has become one of the most prominent research fields in intelligent transportation system (ITS) because of the potential to provide road safety and precautionary measures for the drivers and passengers. In this survey, we discussed the basic overview of the VANET from the architecture, communication methods, standards, characteristics, and VANET security services. Second, we presented the threats and attacks and the recent state-of-the-art methods of the VANET security services. Then, we comprehensively reviewed the authentication schemes that can protect vehicular networks from malicious nodes and fake messages. Third, we discussed the latest simulation tools and the performance of the authentication schemes in terms of simulation tools, which was followed by the VANET applications. Lastly, we identified the open research challenges and gave future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research development. All the security attacks in VANETs and their related countermeasures are discussed with respect to ensuring secure communication. The authentication schemes and comprehensive applications were introduced and analyzed in detail. In addition, open research challenges and future research directions were issued.

Detection and Prevention of DoS Attacks in VANET with RSU’s Cooperative Message Temporal Signature

Vehicular Ad Hoc Networks (VANETs) is an emerging wireless data communication technology in computer network, which communicates dynamically to exchange and share real time information between vehicles on roads. This network architecture considers vehicles as nodes and information as packets for communication. Vehicles create an autonomous network with assistance of RSU (Road Side Units). This technology supports real time alert systems to broadcast emergency messages to the police, ambulance and drivers of the vehicles in some unexpected situations like traffic emergency, accident, road conditions, vehicle tracking, whether conditions and message monitoring. Since these nodes operate in a physically insecure environment in the range of 100 to 300 meters circumference. Security is a challenging issue for the technology to provide secured prominent approach for routing. Like other networks VANET network is also prone to various attacks. Timing and DoS is done by capturing the messages in VANET and replaying them at later point of time in bulk, such that RSU and vehicle resources are wasted in processing those messages. We need to have a technology by virtue of which network nodes (vehicles) should be smart enough to manage road safety at their own. In this paper RSU assisted DoS attack detection and prevention technique is proposed for VANET. The proposed method is based on RSU message temporal signature with detection and prevention, build over the RSU message attestation technique.

A Macroscopic Traffic Model-based Approach for Sybil Attack Detection in VANETs

Vehicular Ad Hoc Networks (VANETs) are expected to play an important role in our lives. They will improve traffic safety and bring a revolution on the driving experience. However, these benefits are counterbalanced by possible attacks that threaten not only the vehicle’s security, but also passengers lives. One of the most common ones is the Sybil attack, which is more dangerous than others since it could be the starting point of many other attacks in VANETs. This paper proposes a distributed approach allowing the detection of Sybil attacks using the traffic flow theory. The key idea here is that each vehicle will monitor its neighbourhood in order to detect an eventual Sybil attack. This is achieved by comparing between the real accurate speed of the vehicle and the one estimated using the V2V communications with vehicles in the vicinity. This estimated speed is obtained using the traffic flow fundamental diagram of the road’s portion where the vehicles are moving.A mathematical model that evaluates the rate of Sybil attack detection according to the traffic density is proposed. Then, this model is validated through some extensive simulations conducted using the well-known NS3 network simulator together with SUMO traffic simulator.

DDoS Detection Mechanism Using Trust-Based Evaluation System in VANET

It is essential for Vehicular Ad-hoc networks (VANETs) to have reliable vehicles for communication with vehicles. VANET is dynamical network where the vehicles frequently alter their place. Safe routing is of great essence at the time of routing process to fit in shared trust/belief involving these nodes. Occasionally, the malicious node transmits the counterfeit data amid other nodes. To find out trust/belief is deemed to be a difficult task when the malicious nodes try to distort the discovery of route or transmission of data within the network. Researchers have worked extensively to ensure a safe routing process with trust-oriented applications. We develop the framework based on trust with a fresh mechanism to determine DDoS attacks in VANET. The major trust elements in the evaluation of trust are frequency value statistics, trust hypothesis statistics, residual energy, trust policy, and data factor. Based on the trust elements, the generation of trust evaluation matrix takes place. We develop the suggested trust mechanism in an innovative manner to offer the security in a better manner by avoiding the trespassers in the network. The deterrence design by trust evaluation mechanism in combination with a clustering method is proficiently made use for the identification of the attacker and reduction of the price concerning detection method. The suggested system optimizes the utilization of a bandwidth without compromising the security of the nodes in the network.

Man-In-The-Middle Attacks in Vehicular Ad-Hoc Networks: Evaluating the Impact of Attackers' Strategies.

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—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.

A Multivariant Stream Analysis Approach to Detect and Mitigate DDoS Attacks in Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle‐to‐Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.

Security Threats and Privacy Issues in Vehicular Ad-Hoc Network (VANET): Survey and Perspective

In Vehicular Ad Hoc Networks (VANETs) Wireless-equipped vehicles are able to communicate with each other as well as Road-Side Units (RSUs) located at strategic places on the road, this enables the formation of self-organized networks connecting the vehicles and RSUs. The (vehicles) nodes are fast mobile causing the network topology to change frequently and unpredictably. Since VANETs do not really rely on any form of central administration or control, nodes in the wireless range dynamically discover each other and establish connection with each other. Due to the pervasive nature of the mobile nodes, it cannot be assumed that VANETs will always be under the control of their owners; nodes could be stolen or tampered with. Each vehicle (node) acts as an independent router and fault detection and network management becomes distributed and more difficult. The shared wireless medium is accessible to both legitimate and illegitimate users; and this has raise formidable research challenges to providing security for this network. This paper take a selective review of the published research work carried out in the security and privacy issue of VANET between 2003 and 2015 and derived a new perspective into the security and privacy attacks in VANET.

Classification of Security Attacks in VANET: A Review of Requirements and Perspectives

Vehicular Ad Hoc Network (VANET) is a pillar of the envisioned Intelligent Transport System (ITS) and a subset of Mobile Ad Hoc Network that grants the communication in between the vehicles alongside with the absence of established communication infrastructure. Exposure to vulnerabilities of Vehicular Ad-Hoc Network (VANET) has been shown to be related to its nature of the environmental. For this reason, VANET security becomes a critical challenge that need to be resolved. In this paper, we assess the VANET security issues and discuss the challenges in VANET. Equally important, we comparatively review the security requirements, the type of attacks and capabilities of attackers present in VANET.

Cellular Automata-based Improved Ant Colony-based Optimization Algorithm for mitigating DDoS attacks in VANETs

In Vehicular Ad hoc NETworks (VANETs), reliable data dissemination between vehicular nodes necessitate maximum degree of collaboration as they play a significant role for ensuring the core objective of communication. But the malicious action of vehicular nodes may disrupt the established degree of co-operation as they lead to poor performance in spite of high resource utilization. The malicious activity of vehicular nodes like DDoS attack must be detected and resolved in a potential way by identifying optimal nodes and optimal paths using cellular automata that modifies the search ability in a global way for increasing the speed of convergence. To prevent the search from falling into local optimum point and to enhance the convergence speed, global searching potential, an enhanced version of ant colony optimization (ACO) algorithm called Cellular Automata-based Improved Ant Colony-based Optimization Algorithm (CA-IACOA) is propounded. In CA-IACOA, the update rules and dynamic adaptive adjustment technique in pheromones of the traditional ACO is enhanced to a significant level based three threshold level of tolerance. This enhancement in the adopted pheromones mainly for attaining better quality solution through reliable and dynamic increment of pheromone that considers the past history related to the visited paths of the ant agents. Further significant tradeoff that exists between solving quality and solving efficiency is resolved using updated dynamic evaporation factor strategy that aids in rapid convergence. Furthermore, trusted boundary symmetric mutation strategy is incorporated in CA-IACOA for improving mutation and to strengthen mutation efficiency. The simulation experiments of CA-IACOA infers that they are potential in handling DDoS attacks as they obtain quality solutions in identifying optimal nodes and optimal paths for reliable routing.

Review on Various Routing Attacks in Vehicular Adhoc Networks

Review on Various Routing Attacks in Vehicular Adhoc Networks

GDVAN: A New Greedy Behavior Attack Detection Algorithm for VANETs

Vehicular Ad hoc Networks (VANETs), whose main objective is to provide road safety and enhance the driving conditions, are exposed to several kinds of attacks such as Denial of Service (DoS) attacks which affect the availability of the underlying services for legitimate users. We focus especially on the greedy behavior which has been extensively addressed in the literature for Wireless LAN (WLAN) and for Mobile Ad hoc Networks (MANETs). However, this attack has been much less studied in the context of VANETs. This is mainly because the detection of a greedy behavior is much more difficult for high mobility networks such as VANETs. In this paper, we propose a new detection approach called GDVAN (Greedy Detection for VANETs) for greedy behavior attacks in VANETs. The process to conduct the proposed method mainly consists of two phases, which are namely the suspicion phase and the decision phase. The suspicion phase is based on the linear regression mathematical concept while decision phase is based on a fuzzy logic decision scheme. The proposed algorithm not only detects the existence of a greedy behavior but also establishes a list of the potentially compromised nodes using three newly defined metrics. In addition to being passive, one of the major advantages of our technique is that it can be executed by any node of the network and does not require any modification of the IEEE 802.11p standard. Moreover, the practical effectiveness and efficiency of the proposed approach are corroborated through simulations and experiments.

Genetic‐based framework for prevention of masquerade and DDoS attacks in vehicular ad‐hocnetworks

AbstractInter‐vehicular communication needs to be secure and anonymous as the effective and robust operations including security and privacy are critical for the deployment of vehicular ad‐hoc networks (VANETs). The security of VANETs has become an imperative matter of consideration, which is prone to disparate security threats. The masquerade attacks and distributed denial of service attacks are among them that hinders the availability of the network. In this paper, a secure genetic‐based framework is proposed for the detection and prevention of masquerade and distributed denial of service attacks in VANETs. The proposed framework is very efficient in the detection and prevention of network anomalies that can affect the network performance. The proposed scheme is evaluated over genetic model defined using Gaussian modelling. The analysis and evaluation of the adduced framework is also presented in the paper. Copyright © 2016 John Wiley &amp; Sons, Ltd.