Enhancing Security in VANETs with Efficient Sybil Attack Detection using Fog Computing
This work addresses security issues in vehicular networks for road safety, but it is incremental as it builds on prior detection methods with specific improvements.
The paper tackles the problem of Sybil attacks in VANETs, which create fake traffic congestion and can lead to collisions, by proposing a fog computing-based detection framework that reduces processing delays by 43%, overhead by 13%, and false-positive rate by 35% at high vehicle densities compared to existing schemes.
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.