T-VNets: a novel Trust architecture for Vehicular Networks using the standardized messaging services of ETSI ITS

In this paper we propose a novel trust establishment architecture fully compliant with the ETSI ITS standard which takes advantage of the periodically exchanged beacons (i.e. CAM) and event triggered messages (i.e. DENM). Our solution, called T-VNets, allows estimating the traffic density, the trust among entities, as well as the dishonest nodes distribution within the network. In addition, by combining different trust metrics such as direct, indirect, event-based and RSU-based trust, T-VNets is able to eliminate dishonest nodes from all network operations while selecting the best paths to deliver legal data messages by taking advantage of the link duration concept. Since our solution is able to adapt to environments with or without roadside units (RSUs), it can perform adequately both in urban and highway scenarios. Simulation results evidence that our proposal is more efficient than other existing solutions, being able to sustain performance levels even in worst-case scenarios. © 2016 Published by Elsevier B.VThis work was partially supported by both the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R, and the Ministere de l'enseignement superieur et de la recherche scientifique, Programme National Exceptionnel P.N.E 2015/2016, Algeria.Kerrache, CA.; Lagraa, N.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2016). T-VNets: a novel Trust architecture for Vehicular Networks using the standardized messaging services of ETSI ITS. Computer Communications. 93:68-83. https://doi.org/10.1016/j.comcom.2016.05.013S68839

MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles

Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

TACASHI: Trust-Aware Communication Architecture for Social Internet of Vehicles

[EN] The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.Kerrache, CA.; Lagraa, N.; Hussain, R.; Ahmed, SH.; Benslimane, A.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.... (2019). TACASHI: Trust-Aware Communication Architecture for Social Internet of Vehicles. IEEE Internet of Things. 6(4):5870-5877. https://doi.org/10.1109/JIOT.2018.2880332S587058776

Trust Management for Vehicular Networks: An Adversary-Oriented Overview

© 2016 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more informationCooperative Intelligent Transportation Systems, mainly represented by vehicular ad hoc networks (VANETs), are among the key components contributing to the Smart City and Smart World paradigms. Based on the continuous exchange of both periodic and event triggered messages, smart vehicles can enhance road safety, while also providing support for comfort applications. In addition to the different communication protocols, securing such communications and establishing a certain trustiness among vehicles are among the main challenges to address, since the presence of dishonest peers can lead to unwanted situations. To this end, existing security solutions are typically divided into two main categories, cryptography and trust, where trust appeared as a complement to cryptography on some specific adversary models and environments where the latter was not enough to mitigate all possible attacks. In this paper, we provide an adversary-oriented survey of the existing trust models for VANETs. We also show when trust is preferable to cryptography, and the opposite. In addition, we show how trust models are usually evaluated in VANET contexts, and finally, we point out some critical scenarios that existing trust models cannot handle, together with some possible solutions.This work was supported by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R.Kerrache, CA.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Lagraa, N.; Manzoni, P. (2016). Trust Management for Vehicular Networks: An Adversary-Oriented Overview. IEEE Access. 4:9293-9307. https://doi.org/10.1109/ACCESS.2016.2645452S92939307

Sécurité des Systèmes Distribués Virtualisés : De la Modélisation au Déploiement

This Thesis deals with security for virtualized distributed environments such as Clouds. In these environments, a client can access resources or services (compute, storage, etc.) on-demand without prior knowledge of the infrastructure underneath. These services are low-cost due to the mutualization of resources. As a result, the clients share a common infrastructure. However, the concentration of businesses and critical data makes Clouds more attractive for malicious users, especially when considering new attack vectors between tenants.Nowadays, Cloud providers offer default security or security by design which does not fit tenants' custom needs. This gap allows for multiple attacks (data thieft, malicious usage, etc.)In this Thesis, we propose a user-centric approach where a tenant models both its security needs as high-level properties and its virtualized application. These security objectives are based on a new logic dedicated to expressing system-based information flow properties. Then, we propose security-aware algorithm to automatically deploy the application and enforce the security properties. The enforcement can be realized by taking into account shared resources during placement decision and/or through the configuration of existing security mechanisms.Cette thèse s'intéresse à la sécurité des environnements virtualisés distribués type “Clouds” ou informatique en nuage. Dans ces environnements, le client bénéficie de ressources ou services (de calcul, stockage, etc.) à la demande sans connaissance de l'infrastructure sous-jacente. Ces services sont proposés à bas coût en mutualisant les ressources proposées aux clients. Ainsi, ces derniers se retrouvent à partager une infrastructure commune. Cependant, cette concentration des activités en fait une cible privilégiée pour un attaquant, d'autant plus intéressante que les Clouds présentent de nouveaux vecteurs d'attaque entre les clients du Clouds de part le partage des ressources. Actuellement, les fournisseurs de solutions de Cloud proposent une sécurité par défaut ne correspondant pas nécessairement aux besoins de sécurité des clients. Cet aspect est donc bien souvent négligé et cette situation donne lieu à de nombreux exemples d'attaques (vol de données, usage malicieux, etc.). Dans cette thèse, nous proposons une approche où le client spécifie ses besoins de sécurité ainsi que son application virtualisée au sein d'un modèle. Nous proposons notamment une nouvelle logique dédiée à l'expression de propriétés sur la propagation de l'information dans un système.Puis, nous proposons un déploiement automatique de ce modèle sur une infrastructure de type Cloud basée sur la virtualisation grâce à nos nouveaux algorithmes prenant en compte les propriétés de sécurité. Ces dernières sont assurées via un placement prenant en compte les risques d'attaques entre ressources partagées et/ou via la configuration de mécanismes de sécurité existants au sein du système

Real time collision warning system in the context of vehicle-to-vehicle data exchange based on drivings behaviours analysis

Worldwide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error regardless of technological innovations and advancements for vehicle safety. Consequently, there is a need for a reliable-real time warning system that can alert drivers of a potential collision. Vehicle-to-Vehicle (V2V) is an extensive area of ongoing research and development which has started to revolutionize the driving experience. Driving behaviour is a subject of extensive research which gains special attention due to the relationship between speeding behaviour and crashes as drivers who engage in frequent and extreme speeding behaviour are overinvolved in crashes. National Highway Traffic Safety Administration (NHTSA) set guidelines on how different vehicle automation levels may reduce vehicle crashes and how the use of on-board short-range sensors coupled with V2V technologies can help facilitate communication among vehicles. Based on the previous works, it can be seen that the assessment of drivers’ behaviours using their trajectory data is a fresh and open research field. Most studies related to driving behaviours in terms of acceleration�deceleration are evaluated at the laboratory scale using experimental results from actual vehicles. Towards this end, a five-stage methodology for a new collision warning system in the context of V2V based on driving behaviours has been designed. Real-time V2V hardware for data collection purposes was developed. Driving behaviour was analyzed in different timeframes prior obtained from actual driving behaviour in an urban environment collected from OBD-II adapter and GPS data logger of an instrumented vehicle. By measuring the in-vehicle accelerations, it is possible to categorize the driving behaviour into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. When the vehicle is in a risk situation, the system based on NRF24L01+PA/LNA, GPS, and OBD-II will pass a signal to the driver using a dedicated LCD and LED light signal. The driver can instantly decide to make the vehicle in a safe mood, effectively avoid the happening of vehicle accidents. The proposed solution provides two main functions: (1) the detection of the dangerous vehicles involved in the road, and (2) the display of a message informing the driver if it is safe or unsafe to pass. System performance was evaluated to ensure that it achieved the primary objective of improving road safety in the extreme behaviour of the driver in question either the safest (or the least aggressive) and the most unsafe (or the most aggressive). The proposed methodology has retained some advantages for other literature studies because of the simultaneous use of speed, acceleration, and vehicle location. The V2V based on driving behaviour experiments shows the effectiveness of the selected approach predicts behaviour with an accuracy of over 87% in sixty-four real-time scenarios presented its capability to detect behaviour and provide a warning to nearby drivers. The system failed detection only in few times when the receiving vehicle missed data due to high speed during the test as well as the distances between the moving vehicles, the data was not received correctly since the power transmitted, the frequency range of the signals, the antenna relative positions, and the number of in-range vehicles are of interest for the V2V test scenarios. The latter result supports the conclusion that warnings that efficiently and quickly transmit their information may be better when driver are under stress or time pressure