Segregation of Sybil Attack using Neighbouring Information in VANET

Panchal, Anu; Singh, Dinesh Kumar

doi:10.17148/iarjset.2017.4631

Cited by 6 publications

(2 citation statements)

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“…Compared with the overhead of direct identity detection mechanisms, indirect identity detection mechanisms are more lightweight, such as the detection methods based on vehicle cooperation [18,21,[34][35][36]. Panchal [37] proposed a method of separating Sybil attacks using adjacent information in VANETs. In this scheme, RSU discovers its neighbor vehicles through the vehicle ID and then evaluates the trust degree of neighbor vehicles to detect Sybil attacks.…”

Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Zhu,

Zeng,

Weng

et al. 2024

Sensors

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Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle’s credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.

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Section: Indirect Identity Detection Mechanismsmentioning

confidence: 99%

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles

Zhu,

Zeng,

Weng

et al. 2024

Sensors

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“…Only after verification, the vehicles are able to send or receive messages [5]. Panchal et al [9] propose the RSU based identification. Kamal et al [10] proposed a session key-based certification method to detect the Sybil attack.…”

Section: Related Workmentioning

confidence: 99%

Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance

Chaubey

Yadav

2022

IJECE

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<span>Vehicular ad hoc network (VANET) is an emerging technology which can be very helpful for providing safety and security as well as for intelligent transportation services. But due to wireless communication of vehicles and high mobility it has certain security issues which cost the safety and security of people on the road. One of the major security concerns is the Sybil attack in which the attacker creates dummy identities to gain high influence in the network that causes delay in some services and fake voting in the network to misguide others. The early detection of this attack can prevent people from being misguided by the attacker and save them from getting into any kind of trap. In this research paper, Sybil attack is detected by first applying the Poisson distribution algorithm to predict the traffic on the road and in the second approach, analysis of the network performance for packet delivery ratio (PDR) is performed in malign and benign environment. The simulation result shows that PDR decreases in presence of fake vehicles in the network. Our approach is simple and effective as it does not require high computational overhead and also does not violate the privacy issues of people in the network.</span>

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