Sybil attack detection in VANET using neighbouring vehicles

Vehicular ad hoc networks (VANETs) are being increasingly advocated for traffic control, accident avoidance, and management of parking lots and public areas. Security and privacy are two major concerns in VANETs. Unfortunately, in VANETs, most privacy-preserving schemes are vulnerable to Sybil attacks, whereby a malicious user can pretend to be multiple (other) vehicles. In this paper, we present a lightweight and scalable protocol to detect Sybil attacks. In this protocol, a malicious user pretending to be multiple (other) vehicles can be detected in a distributed manner through passive overhearing by s set of fixed nodes called road-side boxes (RSBs). The detection of Sybil attacks in this manner does not require any vehicle in the network to disclose its identity; hence privacy is preserved at all times. Simulation results are presented for a realistic test case to highlight the overhead for a centralized authority such as the DMV, the false alarm rate, and the detection latency. The results also quantify the inherent trade-off between security, i.e., the detection of Sybil attacks and detection latency, and the privacy provided to the vehicles in the network. From the results, we see our scheme being able to detect Sybil attacks at low overhead and delay, while preserving privacy of vehicles.

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

A type of ad hoc network formed among moving vehicles that come in one another’s radio transmission range is called Vehicular Ad Hoc Network (VANET). VANET may be deployed using three architectures: pure ad hoc, WLAN and hybrid. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) are the two modes in VANET used for communication among vehicles. These features not only distinguish a VANET from other ad hoc networks but also make these networks more exposed to attacks and increase their complexity. These networks being the primary mechanism for communication in VANETs, appropriate and timely delivery of information is of prime importance. Due to the existence of various vehicular traffic scenarios, a single category of routing protocols is not sufficient for the VANETs. Therefore, various categories of routing protocols have been tailored to meet specific kinds of routing requirements in this framework. Most significant ones are ad hoc/topology-based, position-based, geocast-based, cluster-based, broadcast-based protocols. There exist a number of attacks that apart from affecting various other parameters also affect the routing protocols in these VANETs. Most of these attacks may be launched in all the above-mentioned three architectures. But, one of the most dangerous attacks is the Sybil attack that may be initiated in pure ad hoc deployment of VANET where vehicles communicate with one another in one to one manner using carry forward approach. In this paper, with the help of illustrative example for each of the five categories of routing protocols, we show how Sybil attack affects these protocols. Considering the ad hoc scenarios in VANETs, we also discuss a prevention mechanism for the Sybil attack briefly.

With increase in human population and economic development, population of private vehicles on road has also increased. This has resulted in increased accident probability and fatalities on the roads. Vehicular Ad-hoc Networks (VANETs) have shown promise of bringing down road accidents and fatalities thereof by enabling communication between vehicles. VANETs also allow the road operators to control and monitor vehicles for rash driving and quick relief. However, these networks face challenges of Design, Routing, Communication and Security. Use of wireless medium for communication has left these networks vulnerable to different type of security attacks. One of the important vulnerability in such networks is caused when a malicious vehicle or RSU is able to acquire multiple identities. The attack is termed as Sybil Attack. In VANETs, a malicious vehicle may send wrong messages related to traffic, accident ahead, road closed, etc. This may force the motor driver to take a different route thus making him prone to an untoward incident. In this paper, we present a survey on different detection schemes for detecting Sybil attack. Comparison among these schemes is also presented. Keywords: Vehicular Ad hoc Networks (VANETs), VANETs Architecture, Security Attacks, Sybil Attack detection.

In traffic safety related application of Vehicular Ad-hoc Networks (VANETs), security is a great important issue. Sybil attack is a particular kind of attack where the attacker illegitimately claims multiple identities. In the past years, several approaches have been proposed for solving this problem. They are categorized into PKI-based, infrastructure-based, observer-based, and resource-test-based schemes. In this paper, previous protocols are analyzed, and a novel scheme to detect the Sybil nodes in VANETs is presented, mitigating the effect of a Sybil attack. The proposed Sybil nodes detection scheme, Traffic Flow Theory Aided Physical Measurement-Based Sybil Nodes Detection Mechanism in VANETs (PMSD), takes advantage of unmodifiable physical measurements of the beacon messages instead of key-based materials, which dose not only solve the Sybil attack problem, but also reduces the overhead for the detection. The proposed scheme does not require fixed infrastructure, which makes it easy to implement. To increase the detection accuracy, traffic flow theory and safety guard distance is introduced into the scheme. The simulation results show a 97% detection rate of Sybil nodes, with only about a 2% error rate.

Vehicular ad hoc networks (VANETs) are expected to play an important role in our lives. They will improve the traffic safety and bring about 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 attacks is the Sybil attack, which is even more dangerous than others because it could be the starting point of many other attacks in VANETs. This paper proposes a distributed approach allowing the detection of Sybil attacks by using the traffic flow theory. The key idea here is that each vehicle will monitor its neighborhood in order to detect an eventual Sybil attack. This is achieved by a comparison 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.

Sybil attacks, where malicious nodes use multiple identities, pose a critical threat to Vehicular Ad-Hoc Networks (VANETs). While Received Signal Strength Indicator (RSSI)-based detection offers a promising approach, existing research is hampered by the lack of realistic datasets that capture the complexities of real-world vehicular environments. Current datasets often simplify traffic conditions, neglecting the impact of congestion and packet drops on RSSI readings. This work addresses this gap by creating a large-scale, realistic VANET simulation environment for generating RSSI data. Using OpenStreetMap data for a region of Istanbul, we model realistic traffic flows calibrated with municipal data, incorporating packet loss due to congestion and interference. Critically, we employ a carefully calibrated log-normal shadowing model to capture the impact of environmental factors on RSSI. We implement four common Sybil attack methods with various power control strategies, creating a diverse set of attack scenarios. Rigorous validation against real-world traffic patterns, RSSI characteristics, attack distribution, and packet collisions demonstrates the realism of our dataset. We provide a sample dataset, along with our open-source simulation environment, enabling researchers to develop and evaluate robust Sybil attack detection mechanisms for real-world VANETs

The advancement of wireless communication leads researchers to conceive and develop the idea of vehicular networks, also known as vehicular ad hoc networks (VANETs). In Sybil attack, the WSN is destabilized by a malicious node which create an innumerable fraudulent identities in favor of disrupting networks protocols. In this paper, a novel technique has been proposed to detect and isolate Sybil attack on vehicles resulting in proficiency of network. It will work in two-phases. In first phase RSU registers the nodes by identifying their credentials offered by them. If they are successfully verified, second phase starts & it allots identification to vehicles thus, RSU gathers information from neighboring nodes & define threshold speed limit to them & verify the threshold value is exceed the defined limit of speed. A multiple identity generated by Sybil attack is very harmful for the network & can be misused to flood the wrong information over network. Simulation results show that proposed detection technique increases the possibilities of detection and reduces the percentage of Sybil attack.

Vehicular Ad Hoc Networks (VANETs) is a promising technology for autonomous driving that provides many benefits to the user’s conveniences by improving road safety and driving comfort. However, Sybil attack is a major threat in VANETs where an attacker uses multiple fake identities to send false messages, disrupting safety-related applications. To overcome this, a novel jelly Fish Optimized deep Learning for Sybil Attack DETECTion (FOSA-DETECT) has been proposed to detect sybil attack in VANETs and raise the traffic efficiency. The proposed FOSA-DETECT method utilizing the Jelly Fish Optimization for feature extraction to enhance classification accuracy. Convolutional Neural Network (CNN) based Long Short-Term Memory (LSTM) classification technique classifies the extracted feature into two classes such as Normal and Attacked. Measures including specialty, F1score (F1S), Accuracy, Precision (PR), Recall (RC) are used to assess the suggested approach. Compared to current models, experimental results using VeReMi datasets show higher accuracy. The accuracy of the FOSA-DETECT in the VeReMi dataset is 1.6%, 2.8%, and 2.1% higher than that of the current MDFD, MA-DQN and I-LeeNet techniques respectively

Vehicular Ad-Hoc Network (VANET) is a form of Peer-to-Peer (P2P) wireless communication between vehicles, which is characterized by the high mobility. In practice, VANET can be utilized to cater connections via multi-hop communication between vehicles to provide traffic information seamlessly, such as traffic jam and traffic accident, without the need of dedicated centralized infrastructure. Although dedicated infrastructures may also be involved in VANET, such as Road Side Units (RSUs), most of the time VANET relies solely on Vehicle-to-Vehicle (V2V) communication, which makes it vulnerable to several potential attacks in P2P based communication, as there are no trusted authorities that provide authentication and security. One of the potential threats is a Sybil attack, wherein an adversary uses a considerable number of forged identities to illegitimately infuse false or biased information which may mislead a system into making decisions benefiting the adversary. Avoiding Sybil attacks in VANET is a difficult problem, as there are typically no trusted authorities that provide cryptographic assurance of Sybil resilience. This paper presents a technique to detect and mitigate Sybil attacks, which requires no dedicated infrastructure, by utilizing just V2V communication. The proposed method work based on underlying assumption that says the mobility of vehicles in high vehicle density and the limited transmission power of the adversary creates unique groups of vehicle neighbors at a certain time point, which can be calculated in a statistical fashion providing a temporal and spatial analysis to verify real and impersonated vehicle identities. The proposed method also covers the mitigation procedures to create a trust model and announce neighboring vehicles regarding the detected tempered identities in a secure way utilizing Diffie-Hellman key distribution. This paper also presents discussions concerning the proposed approach with regard to benefits and drawbacks of sparse road condition and other potential threats.

The Sybil attack is a particularly harmful threat to sensor networks where a single sensor node illegitimately claims multiple identities. A malicious node may generate an arbitrary number of additional node identities using only one physical device. The Sybil attack can disrupt normal functioning of the sensor network, such as the multipath routing, used to explore the multiple disjoint paths between source-destination pairs. But the Sybil attack can disrupt it when a single adversary presents multiple identities, which appear on the multiple paths. Digital certificates are a way to prove identities, but they are not suitable for the sensor network because of the large computational overheads. Researchers have proposed a light-weight identity certificate method to defeat Sybil attacks, but it is not suitable for a large scale sensor network because of the huge memory usage required at each node. In this paper, we propose a light-weight Sybil attack detection method based on a hierarchical architecture in sensor networks. The proposed method can be used in a large scale sensor network, and it only uses the symmetric cryptography avoiding the use of the public key cryptography. Simulation results show that the proposed method only needs a small memory (below 140 KB) for each node (even in a large scale sensor network). In addition, the energy consumption by new identity generations within each group is low (below 60 mJ), which is much lower than the available energy at each sensor node.

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.

Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.

Vehicular ad hoc networks (VANETs) play a critical role in enabling intelligent transportation systems, but their open and dynamic nature also makes them highly vulnerable to security threats. Traditional rule-based security mechanisms often fall short of identifying complex or evolving attack patterns. This study explores the application of Deep Belief Networks (DBNs) for automated feature learning and anomaly detection in VANET environments. By leveraging the hierarchical structure of DBNs, the model is trained to extract meaningful temporal and spatial features from high-dimensional traffic and communication data. These learned representations are then used to identify potential malicious behaviors such as spoofing, Sybil attacks, and false message injections. Experimental evaluations on benchmark VANET datasets demonstrate that DBNs significantly improve detection accuracy while reducing false positives compared to shallow learning approaches. This work highlights the potential of deep learning-based models in building more adaptive and resilient VANET security frameworks.

Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs.