Misbehavior Reporting Protocol for C-ITS

Misbehavior detection is a challenging problem that needs to be addressed in vehicular communications. Misbehavior detection consists of monitoring the semantic of the exchanged messages to identify potential misbehaving entities. This is achieved by performing plausibility and consistency checks on exchanged beacon and warning messages. However, existing misbehavior detection solutions ignore the mandatory information on data inaccuracy, being gathered by the vehicular sensors. In this paper, we propose CaTch, an embedded misbehavior detection solution that integrates data inaccuracy when performing plausibility and consistency checks. Through extensive simulations, we show that CaTch is able to attribute an accurate uncertainty factor to misbehaving nodes and that it performs better than the state-of-the-art solutions.

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“…A complete set of plausibility detectors for CAM is detailed in [12]. However in this paper we only consider the following checks:…”

Section: A the Local Misbehavior Detectorsmentioning

confidence: 99%

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Kamel

Kaiser

Jemaa

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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“…We propose a misbehavior report inspired by the protocol described in [26]. The report is composed of three containers: Header Container, Source Container and Evidence Container.…”

Section: Reportingmentioning

confidence: 99%

“…It could also include some other data like a Local Dynamic Map (LDM), or direct sensor data from the reporter. The Evidence required by the MA is further detailed in [26].…”

Section: Reportingmentioning

confidence: 99%

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Simulation Framework for Misbehavior Detection in Vehicular Networks

Kamel

Ansari

Petit

et al. 2020

IEEE Trans. Veh. Technol.

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Cooperative Intelligent Transport Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Unit (RSU) cooperate by broadcasting V2X messages over the vehicular network. Safety applications use these data to detect and avoid dangerous situations on time. MisBehavior Detection (MBD) in C-ITS is an active research topic which consists of monitoring data semantics of the exchanged Vehicle-to-X communication (V2X) messages to detect and identify potential misbehaving entities. The detection process consists of performing plausibility and consistency checks on the received V2X messages. If an anomaly is detected, the entity may report it by sending a Misbehavior Report (MBR) to the Misbehavior Authority (MA). The MA will then investigate the event and decide to revoke the sender or not. In this paper, we present a MisBehavior Detection (MBD) simulation framework that enables the research community to develop, test, and compare MBD algorithms. We also demonstrate its capabilities by running example scenarios and discuss their results. Framework For Misbehavior Detection (F 2 MD) is open source and available for free on our github.

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“…The results of these checks are then analyzed by a set of misbehavior detection algorithms. If an anomaly is detected, the ITS-S reports it by sending a Misbehavior Report (MBR) [2] to the MA. Global detection starts after the reception of these MBRs by the MA.…”

Section: Introductionmentioning

confidence: 99%

A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Kamel

Haidar

Jemaa

et al. 2019

2019 IEEE 10th Annual Ubiquitous Computing, Electronics &Amp; Mobile Communication Conference (UEMCON)

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Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.

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Misbehavior Reporting Protocol for C-ITS

Cited by 17 publications

References 3 publications

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Simulation Framework for Misbehavior Detection in Vehicular Networks

A Misbehavior Authority System for Sybil Attack Detection in C-ITS

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