An Attack Resistance Model for Trustworthiness Evaluation in VANET

Bhanja, Urmila

doi:10.1109/indicon49873.2020.9342302

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…So, careful selection of threshold value is main issue in the proposed paper [18]. Author in [19], also has developed an attack resistant model to counteract different types of VANET attacks such as Sybil attack, replay attack, denial of service, masquerade attack, message suppression attack. Additionally, trustworthiness of vehicles are evaluated based on rate of updation of vehicle, unique ID of vehicle and duration of presence of a vehicle in the VANET.…”

Section: Introductionmentioning

confidence: 99%

Detection of Sybil & DDoS Attacks in VANET using Intelligent Technique

Bhanja,

Majhi,

Sahoo

et al. 2023

Preprint

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Vehicular Adhoc network (VANET) are more prone to various types of attacks. Sybil attack is the most dangerous attack in vehicular adhoc network as it creates multiple fake identities and creates traffic congestion.Fake identities are used to enter the network illegally.On the other hand, distributed denial of service (DDoS) attack intentionally blocks the users from accessing any online services. It temporarily disrupts or interrupts the service of the hosting server. These types of attacks in VANET cause severe damage to vehicles, passengers travelling in the vehicles by inducing traffic congestion, and may also cause minor or fatal accidents.Hence, it is highly essential to early detect such attacks in VANET to protect the vehicles and human kind. In this work, a novel model is proposed using fuzzy logic controllers (FLCs) to detect both the Sybil and the DDoS attacks in VANET. Furthermore, performance of attack detection is also analysed and compared with the existing techniques. The proposed model yields better accuracy, sensitivity, and recall value compared to the existing techniques. Margin of error for the attack detection is also estimated for 95% of the confidence interval.

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Section: Introductionmentioning

confidence: 99%