Formal Analysis of V2X Revocation Protocols

Research on vehicular networking (V2X) security has produced a range of security mechanisms and protocols tailored for this domain, addressing both security and privacy. Typically, the security analysis of these proposals has largely been informal. However, formal analysis can be used to expose flaws and ultimately provide a higher level of assurance in the protocols. This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials. This revocation needs to be performed in an unlinkable way for vehicle privacy even in the context of vehicles regularly changing their pseudonyms. The REWIRE scheme by Forster et al. and its subschemes BASIC and RTOKEN aim to solve this challenge by means of cryptographic solutions and trusted hardware. Formal analysis using the TAMARIN prover identifies two flaws with some of the functional correctness and authentication properties in these schemes. We then propose Obscure Token (OTOKEN), an extension of REWIRE to enable revocation in a privacy preserving manner. Our approach addresses the functional and authentication properties by introducing an additional key-pair, which offers a stronger and verifiable guarantee of successful revocation of vehicles without resolving the long-term identity. Moreover OTOKEN is the first V2X revocation protocol to be co-designed with a formal model.Comment: 16 pages, 4 figure

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

Improvement in Quality of Service Against Doppelganger Attacks for Connected Network

Because they are in a high-risk location, remote sensors are vulnerable to malicious ambushes. A doppelganger attack, in which a malicious hub impersonates a legitimate network junction and then attempts to take control of the entire network, is one of the deadliest types of ambushes. Because remote sensor networks are portable, hub doppelganger ambushes are particularly ineffective in astute wellness contexts. Keeping the framework safe from hostile hubs is critical because the information in intelligent health frameworks is so sensitive. This paper developed a new Steering Convention for Vitality Effective Systems (SC-VFS) technique for detecting doppelganger attacks in IoT-based intelligent health applications such as a green corridor for transplant pushback. This method's main advantage is that it improves vitality proficiency, a critical constraint in WSN frameworks. To emphasize the suggested scheme's execution, latency, remaining vitality, throughput, vitality effectiveness, and blunder rate are all used. To see how proper the underutilized technique is compared to the existing Half Breed Multi-Level Clustering (HMLC) computation. The suggested approach yields latency of 0.63ms and 0.6ms, respectively, when using dead hubs and keeping a strategic distance from doppelganger assault. Furthermore, during the 2500 cycles, the suggested system achieves the highest remaining vitality of 49.5J

A credibility score algorithm for malicious data detection in urban vehicular networks

This paper introduces a method to detect malicious data in urban vehicular networks, where vehicles report their locations to road-side units controlling traffic signals at intersections. The malicious data can be injected by a selfish vehicle approaching a signalized intersection to get the green light immediately. Another source of malicious data are vehicles with malfunctioning sensors. Detection of the malicious data is conducted using a traffic model based on cellular automata, which determines intervals representing possible positions of vehicles. A credibility score algorithm is introduced to decide if positions reported by particular vehicles are reliable and should be taken into account for controlling traffic signals. Extensive simulation experiments were conducted to verify effectiveness of the proposed approach in realistic scenarios. The experimental results show that the proposed method detects the malicious data with higher accuracy than compared state-of-the-art methods. The improved accuracy of detecting malicious data has enabled mitigation of their negative impact on the performance of traffic signal control

Self-reliant misbehavior detection in V2X networks

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems

Segurança das comunicações V2X em ambientes 5G

Estamos à beira de uma nova era de veículos autônomos interligados com experiências de utilizadores e segurança rodoviária melhorada em diversos casos de utilização. Esta tese apresenta conceitos baseando-se no estudo, análise da segurança dos novos sistemas de comunicação sem fios para os sistemas de transporte inteligentes, que consistem em exploração de várias tecnologias com o propósito de melhorar a interface entre condutor, o veículo e a estrada. O objetivo dos sistemas de transporte inteligentes é reduzir significativamente os acidentes de viação, o controlo do tráfego e a poluição do trânsito. Os protocolos de comunicação existentes veículo para todos (V2X), especialmente o 5G, permitiram avanços significativos na segurança de condução autónoma. As aplicações de condução autónoma precisam de informação para chegarem ao seu destino o mais rapidamente possível. Com isto em mente, o V2X oferece múltiplas opções de largura de banda e os recursos de transmissão são partilhados entre utilizadores o que permite uma experiência significativamente aprimorada, inteligente e capaz de suportar a troca massiva de informações de forma rápida e com baixa latência. O 5G-V2X, que é um complemento eficaz do LTE V2X e suporta aplicações de condução autónoma que não podem ser suportadas pelo LTE V2X, também inclui bandas mmWave, gama de subtransportadores escaláveis e massive MIMO. Esta tese visa compreender os mecanismos de segurança das comunicações V2X num ambiente 5G, a forma como esta segurança é proporcionada, as suas consequências positivas e negativas, e os benefícios, riscos e impactos da utilização de comunicações 5G para V2X. O objetivo deste documento é discutir os mecanismos utilizados para garantir a segurança das comunicações V2