Enhancing Security in VANETs with Efficient Sybil Attack Detection using Fog Computing

TL;DR

This paper introduces a fog computing-based framework for detecting Sybil attacks in VANETs, significantly reducing delays, overhead, and false positives compared to previous methods.

Contribution

It presents a novel fog computing approach utilizing onboard units for efficient, real-time Sybil attack detection in high-density vehicular networks.

Findings

43% lower processing delays

13% lower overhead

35% lower false-positive rate

Abstract

Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. Rogue nodes in VANETs cause a Sybil attack to create an illusion of fake traffic congestion by broadcasting malicious information leading to catastrophic consequences, such as the collision of vehicles. Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose a fog computing-based Sybil attack detection for VANETs (FSDV), which utilizes onboard units (OBUs) of all the vehicles in the region to create a dynamic fog for rogue nodes detection. We aim to reduce the data processing delays, overhead, and FPR in detecting rogue nodes causing Sybil attacks at high vehicle densities. The performance of our framework was carried out with simulations using OMNET++ and SUMO simulators. Results show that our framework ensures 43% lower processing delays, 13% lower overhead, and 35% lower FPR at high vehicle densities compared to existing Sybil attack detection schemes.