research-article

Detecting Location Fraud in Indoor Mobile Crowdsensing

Authors:

Ezzeldin TahounAuthors Info & Claims

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

Pages 44 - 49

https://doi.org/10.1145/3139243.3139252

Published: 06 November 2017 Publication History

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.

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Cited By

Zhang ETrujillo RTempleton JPoellabauer C(2023)A Study on Mobile Crowd Sensing Systems for Healthcare ScenariosIEEE Access10.1109/ACCESS.2023.334215811(140325-140347)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342158
Sun YDing WShu LLi KZhang YZhou ZHan G(2022)On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A VisionIEEE Systems Journal10.1109/JSYST.2021.310410716:1(132-143)Online publication date: Mar-2022
https://doi.org/10.1109/JSYST.2021.3104107
Zhang YSimsek MKantarci B(2021)Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing PlatformsIEEE Internet of Things Journal10.1109/JIOT.2020.30114618:3(1334-1346)Online publication date: 1-Feb-2021
https://doi.org/10.1109/JIOT.2020.3011461
Show More Cited By

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Detecting Location Fraud in Indoor Mobile Crowdsensing

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cover image ACM Conferences

CrowdSenSys '17: Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications

November 2017

81 pages

ISBN:9781450355551

DOI:10.1145/3139243

Editor:
Rasit Eskicioglu
University of Manitoba, Canada

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2017

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Natural Sciences and Engineering Research Council of Canada

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SenSys '17

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SenSys '17: The 15th ACM Conference on Embedded Network Sensor Systems

November 6 - 8, 2017

Delft, Netherlands

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Cited By

Zhang ETrujillo RTempleton JPoellabauer C(2023)A Study on Mobile Crowd Sensing Systems for Healthcare ScenariosIEEE Access10.1109/ACCESS.2023.334215811(140325-140347)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342158
Sun YDing WShu LLi KZhang YZhou ZHan G(2022)On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A VisionIEEE Systems Journal10.1109/JSYST.2021.310410716:1(132-143)Online publication date: Mar-2022
https://doi.org/10.1109/JSYST.2021.3104107
Zhang YSimsek MKantarci B(2021)Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing PlatformsIEEE Internet of Things Journal10.1109/JIOT.2020.30114618:3(1334-1346)Online publication date: 1-Feb-2021
https://doi.org/10.1109/JIOT.2020.3011461
Capponi AFiandrino CKantarci BFoschini LKliazovich DBouvry P(2019)A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and OpportunitiesIEEE Communications Surveys & Tutorials10.1109/COMST.2019.291403021:3(2419-2465)Online publication date: Nov-2020
https://doi.org/10.1109/COMST.2019.2914030
Liu YKong LChen G(2019)Data-Oriented Mobile Crowdsensing: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2019.291085521:3(2849-2885)Online publication date: Nov-2020
https://doi.org/10.1109/COMST.2019.2910855

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