Defending against Sybil Devices in Crowdsourced Mapping Services

TL;DR

This paper identifies security vulnerabilities in crowdsourced mapping services caused by Sybil devices and proposes a detection method based on co-location edges and proximity graphs to mitigate these threats.

Contribution

It introduces a novel defense mechanism using co-location edges and proximity graphs to detect and reduce Sybil device attacks in crowdsourced maps.

Findings

Single Sybil devices can cause significant misinformation in Waze.

Scale of Sybil attacks can be increased with virtual armies of devices.

Proposed detection method effectively reduces impact of Sybil attacks.

Abstract

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.