Cooperative Intelligent Transport Systems (C-ITS) is a new upcoming technology that aims at increasing road safety and reducing traffic accidents. C-ITS is based on peer-to-peer messages sent on the Vehicular Ad hoc NETwork (VANET). VANET messages are currently authenticated using digital keys from valid certificates. However, the authenticity of a message is not a guarantee of its correctness. Consequently, a misbehavior detection system is needed to ensure the correct use of the system by the certified vehicles. Although a large number of studies are aimed at solving this problem, the results of these studies are still difficult to compare, reproduce and validate. This is due to the lack of a common reference dataset. For this reason, the original VeReMi dataset was created. It is the first public misbehavior detection dataset allowing anyone to reproduce and compare different results. VeReMi is used in a number of studies and is currently the only dataset in its field. In this Paper, we extend the dataset by adding realistic a sensor error model, a new set of attacks and larger number of data points. Finally, we also provide benchmark detection metrics using a set of local detectors and a simple misbehavior detection mechanism.

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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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.

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

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.

Misbehavior detection is a set of mechanisms that rely on monitoring C-ITS communications to detect potentially misbehaving entities. In this paper we focus on the reporting process of Misbehavior Detection. More precisely, we propose a misbehavior report message format that enables an entity to report a detected misbehaving entity. We explain first the functional requirements of a misbehavior reporting mechanism. Then, we detail the data information that are integrated in the reports in order to provide reliable evidences to the misbehavior authority.

MisBehavior Detection (MBD) is an important security mechanism in Cooperative Intelligent Transport Systems (C-ITS). It involves monitoring C-ITS communications to detect potentially misbehaving entities. This monitoring is based on local plausibility and consistency checks done by the Intelligent Transport Systems (ITS) Station (ITS-S) on every received Vehicle-to-Everything (V2X) message. These checks are then analyzed by a local detection mechanisms to estimate the overall plausibility of a message. In this paper we focus on the logic behind different local detection mechanisms. First, we propose different local detection solutions based on logics extracted from the state of the art. Then we present a comparative review of the detection quality and the computation latency of each proposed mechanisms.