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Pearson Correlation Analysis to Detect Misbehavior in VANET

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Vehicular Ad-hoc Networks (VANET) rely on Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication to improve road safety and traffic efficiency. Therefore, malicious data could jeopardize the benefits of VANET communication. Hence, a data-centric misbehavior detection system should be deployed on each on-board unit to improve confidence in the received data. In this paper, we investigate the potential of using Pearson Correlation to detect location forging attacks. We analyze four location forging attacks and discuss how the correlation matrix detect them. The proposed solution works in real-time, without any training, but, depending on the type of road, requires at least four to seven seconds of history to be fully effective. Experiments are performed on real datasets from Wyoming Connected Vehicle Pilot Deployment and from University of Michigan Transportation Research Institute.

Original languageEnglish
Title of host publication2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663585
DOIs
StatePublished - Jul 2 2018
Event88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States
Duration: Aug 27 2018Aug 30 2018

Publication series

NameIEEE Vehicular Technology Conference
Volume2018-August

Conference

Conference88th IEEE Vehicular Technology Conference, VTC-Fall 2018
Country/TerritoryUnited States
CityChicago
Period08/27/1808/30/18

Keywords

  • Misbehavior Detection
  • pearson correlation
  • position forging attacks
  • security
  • vehicular networks

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