Detecting Location Fraud in Indoor Mobile Crowdsensing

TL;DR

This paper addresses indoor mobile crowdsensing security by proposing algorithms to detect tag forgery, misplacement, and removal, using location fingerprints, truth discovery, and visiting patterns, achieving high detection accuracy.

Contribution

It introduces novel detection algorithms combining location fingerprints, truth discovery, and visiting patterns to identify various tag-related attacks in indoor crowdsensing.

Findings

High accuracy in detecting all three attack types

Effective use of location-dependent fingerprints

Successful validation on real and emulated datasets

Abstract

Mobile crowdsensing allows a large number of mobile devices to measure phenomena of common interests and form a body of knowledge about natural and social environments. In order to get location annotations for indoor mobile crowdsensing, reference tags are usually deployed which are susceptible to tampering and compromises by attackers. In this work, we consider three types of location-related attacks including tag forgery, tag misplacement, and tag removal. Different detection algorithms are proposed to deal with these attacks. First, we introduce location-dependent fingerprints as supplementary information for better location identification. A truth discovery algorithm is then proposed to detect falsified data. Moreover, visiting patterns are utilized for the detection of tag misplacement and removal. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy.