Vehicular Ad-hoc Networks (VANETs) serve as the basis of Intelligent Transportation Systems (ITS) enhancing the safety on roads and driving conditions. A rogue node in a VANET may launch Sybil attacks by broadcasting mischievous information to make an impression of false road congestion. Sybil attacks can be the cause for rerouting of vehicles, traffic congestion, collision of vehicles, and be an opening for launching other attacks. Prior research used different approaches such as cryptography, trust scores, past vehicle data, blockchain, machine learning, etc. to detect Sybil attacks. However, the false-positive rate (FPR), processing delay, and overhead of these techniques are high. In this paper, we present a technique for detecting Sybil attacks in VANETs using fog computing and beamforming, called FSDV-H (Fog computing-based Sybil attack Detection in VANETs for Highway scenarios). The onboard units of vehicles in a specific area are used in FSDV-H to create a dynamic fog for spotting potential Sybil attacks based on vehicle speed before validating the suspicions with a beamforming-based location verification. The goals of FSDV-H are to reduce FPR, data processing delay, and overhead at high vehicular density in a highway scenario. Performance evaluations using SUMO and OMNET++ simulators confirm that FSDV-H offers 69% lower FPR, 36% lower processing delay, and 7% lower overhead at high vehicular densities in a highway scenario in comparison to existing Sybil attack detection techniques.
Sybil Attack Detection in VANETs using Fog Computing and Beamforming
2023-10-12
1917137 byte
Conference paper
Electronic Resource
English
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