Detection Technique In Delay Tolerant Networks Using Rid and VDTN Frame Work

Santhosh, B; Lohiya, Harsh; Rani, Beena

doi:10.2139/ssrn.4160221

Cited by 1 publication

(3 citation statements)

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“…The architecture of the VDTN Protocol aims to deliver effective communication even in challenging VANET scenarios with periodic connectivity and dynamic topology [14] [5]. The protocol improves data delivery dependability and aids in overcoming communication issues by utilizing the store-carry-forward mechanism and intelligent forwarding decisions [1] [2].…”

Section: Vdtn Protocol (Vehicular Delay-tolerant Network)mentioning

confidence: 99%

“…To do this, the TD3 algorithm is trained using both real-world VANET traffic data and simulated traffic data with wormhole attacks. The following steps show how it can be used to identify wormhole attacks: Data collection: In VANETs, data is extracted from legitimate vehicles and RSUs to understand normal network behavior [1]. Attack Simulation: By inserting data packets between two malicious vehicles that have a high-speed data transfer tunnel, it is possible to simulate wormhole attack scenarios.…”

Section: Functions Of Td3 In Detecting Wormhole Attacks In Vanetmentioning

confidence: 99%

“…The third component is RSU, which is installed along the roadside and is primarily utilized for data transfer, authentication, and validation. RSUs are used to transmit data between two or more vehicles that want to communicate but are out of range of one another [1] [9]. The three most common communication techniques are V2V (Vehicle to Vehicle), V2I (Vehicle2Infrastructure), and V2X (Hybrid -V2V / V2I) [9].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

2024

KSII TIIS

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Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

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