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Towards a Reliable Machine Learning Based Global Misbehavior Detection in C-ITS: Model Evaluation Approach
Abstract : Global misbehavior detection in Cooperative Intelligent Transport Systems (C-ITS) is carried out by a central entity named Misbe-havior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML) based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.
Cited literature [25 references]
https://hal.archives-ouvertes.fr/hal-02353893
Contributor : Joseph Kamel Connect in order to contact the contributor
Submitted on : Thursday, November 7, 2019 - 2:56:19 PM
Last modification on : Monday, January 24, 2022 - 8:26:32 AM
Long-term archiving on: : Sunday, February 9, 2020 - 12:55:59 AM
Contributor : Joseph Kamel Connect in order to contact the contributor
Submitted on : Thursday, November 7, 2019 - 2:56:19 PM
Last modification on : Monday, January 24, 2022 - 8:26:32 AM
Long-term archiving on: : Sunday, February 9, 2020 - 12:55:59 AM
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