# Detecting Sybil Attacks using Proofs of Work and Location in VANETs

**Authors:** Mohamed Baza, Mahmoud Nabil, Niclas Bewermeier, Kemal Fidan, Mohamed Mahmoud, Mohamed Abdallah  
**arXiv:** [1904.05845](https://arxiv.org/abs/1904.05845)  
**Date:** 2019-04-11  
**Summary page:** https://www.summarizepaper.com/en/paper/1904.05845v1/

## Key points

- Proposed scheme for detecting Sybil attacks in VANETs using proofs of work and location
- Each RSU issues signed time-stamped tags for vehicle's anonymous location
- Multiple consecutive RSUs create trajectory for vehicle, serving as its anonymous identity
- Scheme requires contributions from several RSUs to prevent fake trajectories successfully
- Vehicles must solve computational puzzle using PoW algorithm before obtaining next proof of location
- Event manager uses matching technique to identify Sybil trajectories during reported events like road congestion
- Effectiveness based on physical constraint that Sybil trajectories are bound to one vehicle, requiring overlapping trajectories
- Extensive experiments and simulations show high detection rate for Sybil attacks with minimal false negatives and acceptable overhead

## Summary

In their paper titled "Detecting Sybil Attacks using Proofs of Work and Location in VANETs," Mohamed Baza, Mahmoud Nabil, Niclas Bewermeier, Kemal Fidan, Mohamed Mahmoud, and Mohamed Abdallah propose a novel scheme for detecting Sybil attacks in Vehicular Ad-Hoc Networks (VANETs) using proofs of work and location. The core idea behind their approach is to have each road side unit (RSU) issue a signed time-stamped tag as proof of a vehicle's anonymous location. These tags are then used by multiple consecutive RSUs to create a trajectory for the vehicle, serving as its anonymous identity. Unlike traditional methods where a single RSU can issue trajectories for vehicles, this scheme requires contributions from several RSUs. This means that attackers would need to compromise an impractical number of RSUs to create fake trajectories successfully. Additionally, upon receiving the proof of location from an RSU, the vehicle must solve a computational puzzle using a proof of work (PoW) algorithm before obtaining the next proof of location. This PoW requirement acts as a deterrent against vehicles creating multiple trajectories in scenarios with low-density RSUs. During any reported event such as road congestion, the event manager utilizes a matching technique to identify trajectories sent from Sybil vehicles. The effectiveness of this scheme lies in the physical constraint that Sybil trajectories are bound to one vehicle; hence their trajectories should overlap. Extensive experiments and simulations conducted by the authors demonstrate that their proposed scheme achieves a high detection rate for Sybil attacks with minimal false negatives and acceptable communication and computation overhead. Overall, this innovative approach offers an effective solution for enhancing security in VANETs by leveraging proofs of work and location to detect malicious activities such as Sybil attacks with efficiency and accuracy.