Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

Towards a Reliable Machine Learning Based Global Misbehavior Detection in C-ITS: Model Evaluation Approach

International audienceGlobal 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

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Etude de Faisabilité des Mécanismes de Détection de Mauvais Comportement dans les systèmes de transport intelligents coopératifs (C-ITS)

International audience—Cooperative Intelligent Transport Systems (C–ITS) is an emerging technology that aims at improving road safety, traffic efficiency and drivers experience. To this end, vehicles cooperate with each others and the infrastructure by exchanging Vehicle–to–X communication (V2X) messages. In such communicating systems message authentication and privacy are of paramount importance. The commonly adopted solution to cope with these issues relies on the use of a Public Key Infrastructure (PKI) that provides digital certificates to entities of the system. Even if the use of pseudonym certificates mitigate the privacy issues, the PKI cannot address all cyber threats. That is why we need a mechanism that enable each entity of the system to detect and report misbehaving neighbors. In this paper, we provide a state-of-the-art of misbehavior detection methods. We then discuss their feasibility with respect to current standards and law compliance as well as hardware/software requirements

Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians.  Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics.  In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works