Cooperative Intelligent Transportation Systems (C-ITS) is an ongoing technology that will change our driving experience in the near future. In such systems, vehicles and RoadSide Units (RSU) cooperate by broadcasting V2X messages over the vehicular network (802.11p). Safety applications use these data to detect dangerous situations on time and avoid them. The security of V2X communications is based on the use of a vehicular Public Key Infrastructure (PKI) that delivers digital certificates to vehicles and RSU. Vehicles frequently change their certificate in order to make tracking more difficult and thus preserve drivers privacy. In this paper, we evaluate the performance of our PKI regarding the reloading of certificates by comparing two communication profiles (with and without V2X security). We developed a Proofof-Concept (PoC) with real implementation of the PKI protocol and the embedded system. The obtained results show that the end-to-end latency between a requesting vehicle and the PKI is non-negligible. We then discuss and propose optimizations that can be done to improve the performance of the system.
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.
In the near future vehicles will be connected and able to communicate with their environment. Such technologiescommonly called Cooperative Intelligent Transportation Systems (C-ITS) -aim at improving road safety, traffic efficiency and drivers comfort. To this end the C-ITS community has proposed many different use cases. In this paper, we start by making an inventory of C-ITS use cases. We then extend this list by proposing new use cases mostly related to security and privacy aspects. Finally we propose a classification methodology based on K-means algorithm to classify the use cases according to criteria we defined. We apply the proposed methodology on our use cases list using security and technical criteria. The obtained results enable to extract a subset of representative use cases from the initial list. Such subset can then be used to apply any process/method (e.g. risk analysis) on it.
In the near future, vehicles will communicate with their environment by broadcasting Vehicle to everything (V2x) messages over the vehicular network (IEEE 802.11p). The exchanged messages contain data related to driver's privacy. As the laws in Europe require the privacy protection, the solution is to use pseudonym identities (certificates) in the communication. However, the use of these certificates can create new vulnerabilities that must be taken into account. In this paper, we do a state of art on the existing vulnerabilities, we applied the TVRA method and propose new vulnerabilities. Finally, we propose new countermeasures that could be implemented.
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