Bayesian Game Approach to Mitigate DoS Attack in Vehicular Ad-Hoc Networks

Author(s): Ilavendhan Anandaraj*, Saruladha Krishnamurthy

Journal Name: Recent Patents on Engineering

Volume 15 , Issue 2 , 2021


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Abstract:

Background: Trustful message transmission within the Vehicular ad-hoc Networks is needed as traffic related safety applications needs successful and reliable delivery of messages. The biggest issue is how only the trustworthy parties can be retained and the misbehaved are revoked. From the background analysis, it gives a detailed view of various vulnerabilities in these networks and the techniques used by the researchers to identify and mitigate the attack. Based on the drawbacks observed in the literature the proposed Bayesian Game Mechanism has been designed.

Objective: The major objective of this manuscript is to identify the Denial of Service Attack in Vehicular Ad-hoc Networks as it depletes availability of the resources. This attack is identified using Bayesian Game approach.

Methods: The Bayesian game approach is used to identify the attack. It analyzes the behavior of vehicles and classifies them into trustworthy or malicious.

Results: The simulation is conducted using Network simulator 2.34 and the traffic model is designed using by considering 100 nodes. From the results it is inferred that Packet drop ratio has been improved by 9.82 % and the delay and throughput has been minimized, when the proposed mechanism is used in the presence of attackers.

Conclusion: This paper identifies Denial of Service attack as the most vulnerable attack in this network and has designed game theoretic approach namely Bayesian approach for preventing this attack. The proposed method has minimized the delay and packet drop and improved the throughput when compared against the bench mark.

Keywords: Game theory, cooperative game, non-cooperative game, VANETs, SMDRP, PDA.

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Article Details

VOLUME: 15
ISSUE: 2
Year: 2021
Published on: 27 April, 2020
Page: [150 - 160]
Pages: 11
DOI: 10.2174/1872212114999200428100330
Price: $25

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