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

Chaubey, Nirbhay; Yadav, Dhananjay

doi:10.11591/ijece.v12i2.pp1703-1710

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Chaubey et al 18 estimated traffic on the road using the Poisson distribution, and then examined network performance for PDR in both a malicious and benign environment to discover the Sybil attack. Results reveal that PDR lowers when there are more bogus automobiles on the network.…”

Section: Related Workmentioning

confidence: 99%

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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SummaryAn attack on VANET known as a Sybil attack involves the attacker claiming or stealing multiple identities with the intent of causing havoc on the network's operation by disseminating false information. To protect the VANET network from Sybil attack, a variety of methods have been proposed. In order to compensate for the Sybil attack on VANETs, a rumor riding with salp swarm optimization (SSO) based Routing protocol is developed in this research work. The primary goal of this investigation is to discover how VANETs are vulnerable to the Sybil attack in order to improve their security. Rumor riding will be used to identify the Sybil attack in the suggested strategy. VANETs are protected by the Blowfish Algorithm, which uses SSO to select the best key for each network. The collective intelligence and dynamic adaptation of SALP swarm optimization algorithm enable efficient identification of rumor sources in complex networks. The algorithm is scalable, flexible, and adept at reducing false positives and negatives, making it suitable for large‐scale monitoring. Its parallel processing capabilities and robustness ensure rapid and reliable detection. Using the NS2 platform, the proposed method is compared to existing benchmark in terms of delay, packet delivery ratio (PDR), packet loss, throughput, and attack detection rate. The proposed SSO has reduced the packet loss by nearly 5% and delay by nearly 7%, when compared with existing techniques. The delivery ratio of packet for proposed model is increased up to 8% of PDR, when compared with ACO, PSO, and FA. According to this evidence, it is clear that the proposed SSO outperformed existing approaches in a variety of performance metrics.

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Section: Related Workmentioning

confidence: 99%

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Navinkumar,

Somasundaram

2024

Int J Communication

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“…The basic purpose of vehicular networks is to ensure passenger safety, conductors, and vehicles through the exchange of private accident and traffic information among vehicles [14]. However, malicious vehicle using VANET might disrupt vehicle communication by broadcasting incorrect information and fake alarms [15,16].…”

Section: Introductionmentioning

confidence: 99%

Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET)

Slama¹,

Alaya²,

Zidi³

2022

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C-ITS (Cooperative Intelligent Transport Systems) est une nouvelle technologie qui contribue à la réduction des accidents de la circulation et à l'amélioration de la sécurité routière. VANET ( V ehicular A d hoc Networks) sont un système STI basé sur la communication inter-véhicules par la transmission de messages de sécurité de base (BSM), qui sont vulnérables à une variété de comportements inappropriés. Pour résoudre ce défi, nous avons développé dans cet article un système de détection de mauvais comportement (MDS) basé sur une approche d'apprentissage automatisé pour identifier et catégoriser les messages de mauvais comportement délivrés par un véhicule sur les VANET à l'aide de la base de données d'extension VeReMi. Cette étude examine différents types de classification : dans la classification binaire, toutes sortes d'inconduites ont été regroupées en une seule catégorie « inconduite » ; cependant, dans la classification multi-classes pour trois classes, la mauvaise conduite a été divisée en deux classes : les attaques et les fautes. Le classificateur a des problèmes substantiels lors de l'apprentissage à partir de données déséquilibrées wLorsque vous travaillez avec des problèmes multi-classes, cela devient considérablement plus complexe. Les relations entre les catégories ne sont plus bien définies et il est facile de perdre en efficacité dans une classe tout en s'améliorant dans une autre. En conséquence, les résultats ne sont pas cohérents dans l'approche d'apprentissage classique pour la classification multi-classes lors de la classification des comportements répréhensibles dans différents types de classes de comportements répréhensibles. Pour résoudre ce problème, nous avons développé une approche nouvelle et puissante appelée "Approche d'apprentissage guidé pour la classification multi-classes" pour réduire le nombre de classes en combinant des comportements inappropriés comparables en un seul. Selon les résultats, le classificateur Random Forest surpasse les autres classificateurs.

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Relational Model and Improvised DSR Assisted Framework for Secure Wireless Communication

Manjunatha¹,

S²,

Bhavana³

2023

Communications in Computer and Information Science

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

Cited by 6 publications

References 24 publications

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Developing an optimized routing protocol with rumor riding technique for detection of Sybil attack in VANET environment

Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET)

Relational Model and Improvised DSR Assisted Framework for Secure Wireless Communication

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