Detecting Sybil Attacks in Vehicular Ad Hoc Networks

TL;DR

This paper proposes a hybrid detection algorithm combining footprint and privacy-preserving pseudonym abuse detection to identify Sybil attacks in VANETs, evaluated via ns2 simulations with varying vehicle speeds and numbers.

Contribution

It introduces a novel hybrid detection method for Sybil attacks in VANETs, integrating footprint and privacy-preserving techniques with encryption and trajectory analysis.

Findings

P2DAP outperforms footprint with increasing vehicle numbers.

Footprint performs better at higher vehicle speeds.

Hybrid algorithm effectively detects Sybil attacks using simulation scenarios.

Abstract

Ad hoc networks is vulnerable to numerous number of attacks due to its infrastructure-less nature, one of these attacks is the Sybil attack. Sybil attack is a severe attack on vehicular ad hoc networks (VANET) in which the intruder maliciously claims or steals multiple identities and use these identities to disturb the functionality of the VANET network by disseminating false identities. Many solutions have been proposed in order to defense the VANET network against the Sybil attack. In this research a hybrid algorithm is proposed, by combining footprint and privacy-preserving detection of abuses of pseudonyms (P2DAP) methods. The hybrid detection algorithm is implemented using the ns2 simulator. The proposed algorithm is working as follows, P2DAP acting better than footprint when the number of vehicles increases. On the other hand, the footprint algorithm acting better when the speed of vehicles increases. The hybrid algorithm depends on encryption, authentication and on the trajectory of the vehicle. The scenarios will be generated using SUMO and MOVE tools.