A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing

TL;DR

This paper introduces F-RouND, a fog computing-based scheme utilizing Greenshield's traffic model to detect rogue nodes in VANETs, significantly reducing delays, overhead, and false positives compared to existing methods.

Contribution

The paper presents a novel fog computing framework that dynamically detects rogue nodes in VANETs using Greenshield's traffic model, improving detection speed and accuracy.

Findings

45% lower processing delays

12% lower overhead

36% lower false-positive rate

Abstract

Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False-Positive Rate (FPR). We propose herein Greenshield's traffic model-based fog computing scheme called Fog-based Rogue Nodes Detection (F-RouND), which dynamically utilizes the On-Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F-RouND framework was evaluated via simulations. Results show that the F-RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario compared to the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region.