Generating Semantic Trajectories Using a Car Signal Ontology

Klotz, Benjamin; Troncy, Raphaël; Wilms, Daniel; Bonnet, Christian

doi:10.1145/3184558.3186962

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Among the various ontologies to which we referred, we considered those made available by the Automotive Ontology Community Group, the W3C working group and a group of domain experts of University of Salerno. Other reference ontologies taken into consideration were those present in the research in [50][51][52]. Once the context was identified, it was necessary to define the taxonomy of a car to identify the characteristics of the system and the identification of the parameters to be monitored.…”

Section: Case Of Studymentioning

confidence: 99%

Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles

2021

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Today’s modern vehicles are connected to a network and are considered smart objects of IoT, thanks to the capability to send and receive data from the network. One of the greatest challenges in the automotive sector is to make the vehicle secure and reliable. In fact, there are more connected instruments on a vehicle, such as the infotainment system and/or data interchange systems. Indeed, with the advent of new paradigms, such as Smart City and Smart Road, the vision of Internet of Things has evolved substantially. Today, we talk about the V2X systems in which the vehicle is strongly connected with the rest of the world. In this scenario, the main aim of all connected vehicles vendors is to provide a secure system to guarantee the safety of the drive and persons against a possible cyber-attack. So, in this paper, an embedded Intrusion Detection System (IDS) for the automotive sector is introduced. It works by adopting a two-step algorithm that provides detection of a possible cyber-attack. In the first step, the methodology provides a filter of all the messages on the Controller Area Network (CAN-Bus) thanks to the use of a spatial and temporal analysis; if a set of messages are possibly malicious, these are analyzed by a Bayesian network, which gives the probability that a given event can be classified as an attack. To evaluate the efficiency and effectiveness of our method, an experimental campaign was conducted to evaluate them, according to the classic evaluation parameters for a test’s accuracy. These results were compared with a common data set on cyber-attacks present in the literature. The first experimental results, obtained in a test scenario, seem to be interesting. The results show that our method has good correspondence in the presence of the most common cyber-attacks (DDoS, Fuzzy, Impersonating), obtaining a good score relative to the classic evaluation parameters for a test’s accuracy. These results have decreased performance when we test the system on a Free State Attack.

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Section: Case Of Studymentioning

confidence: 99%

Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles

2021

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“…The taxonomy focuses on conventional sensors, actuators, and data of infotainment systems. [117], [118] Based on VSS (see Section V-C5) and further ontologies, 12 Klotz et al create an extensive ontology for vehicle signals and sensors [118]. The VSS ontology (VSSo) is used in combination with the STEP ontology [119] to map trajectories using the ontology's vocabulary and enrich it with semantic information in the referenced publication.…”

Section: ) ''Vehicle Signal Specification -Standardized Waymentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user's life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these everincreasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.

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“…Tandis que les informations issues d'autres capteurs se focalisent sur l'état du véhicule et ses capacités à fournir un service (niveau de carburant, taux de remplissage d'une benne pour un camion à ordure, etc.). De même, la combinaison des coordonnées GPS et des données issues des capteurs des véhicules permet la recherche de comportements de conduite : éco-conduite (Klotz et al, 2018), etc. Cependant, l'envoi par les véhicules de grandes quantités de données chaque seconde entraîne des problématiques classiques liées au big data.…”

Section: Introductionunclassified

Modélisation et extraction de la sémantique des trajectoires à partir de données multicapteurs

2018

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Petit Picot by GRUAUZI n°2 -18 rue de Prony, 37303 Joué-lès-Tours France prenom.nom@gruau.com RÉSUMÉ. Les positions issues de capteurs GPS sont régulièrement employées pour analyser des déplacements d'objets mobiles. L'emploi exclusif des positions GPS s'avère insuffisant afin de détecter finement les comportements d'objets mobiles, pour des raisons d'imprécisions et de perte d'informations. Les objets mobiles sont maintenant équipés de plus en plus de capteurs complémentaires (génériques ou spécifiques à des types de véhicules). Cet article présente un modèle de trajectoires sémantique ainsi qu'une méthode pour extraire la sémantique liée à celles-ci à partir de données brutes issues de différents capteurs. Ces outils de fouille de trajectoires permettent d'identifier les activités des véhicules lors de déplacements ou d'arrêts. Ce processus a été appliqué aux déplacements d'ambulances connectées des sapeurs-pompiers d'Indre et Loire. ABSTRACT. GPS positions are useful to analyze movements of mobile objects. Unfortunately, the outcome can be unsatisfactory due to imprecision and signal lost. Several sensors (generic or specific ones depending on the type of vehicle) are now included into mobile objects. This article describes a new model for semantic trajectories. This article also presents the movement pattern extraction process based on the raw data from GPS and other sensors, which help identified vehicle activities and movement patterns. This process was successfully applied to the Indre et Loire fire department and its connected ambulances. MOTS-CLES : objet mobile, fouille de trajectoires, trajectoires sémantiques, capteurs GPS, analyse de mobilité, véhicule de secours

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Generating Semantic Trajectories Using a Car Signal Ontology

Cited by 9 publications

References 6 publications

Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles

Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles

Context-Aware Security for Vehicles and Fleets: A Survey

Modélisation et extraction de la sémantique des trajectoires à partir de données multicapteurs

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