Defending against Sybil Devices in Crowdsourced Mapping Services

Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based {\em Sybil devices} that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on {\em co-location edges}, authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large {\em proximity graphs} that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio

Socially Inspired Data Dissemination for Vehicular Ad Hoc Networks

International audiencePeople have routines and their mobility patterns vary during the day, which have a direct impact on vehicular mobility. Therefore, proto-cols and applications designed for Vehicular Ad Hoc Networks need to adapt to these routines in order to provide better services. With this issue in mind, in this work, we propose a data dissemination solution for these networks that considers the daily road traffic vari-ation of large cities and the relationship among vehicles. The focus of our approach is to select the best vehicles to rebroadcast data messages according to social metrics, in particular, the clustering coefficient and the node degree. Moreover, our solution is designed in such a way that it is completely independent of the perceived road traffic density. Simulation results show that, when compared to related protocols, our proposal provides better delivery guarantees, reduces the network overhead and possesses an acceptable delay

Exploring Interactions in Vehicular Networks

International audienceVehicular networks are networks comprised by vehicles trav-eling cities and highways. During their trajectories, these vehicles interact with other vehicles and road side units in order to make safer and enjoyable trac. These interactions may be influenced by several factors. To mention a few: vehicle speed, roads condition, time of day and weather. Moreover, driver behavior and its interests can influence in vehicle features. In this context, the Vehicular Social Networks arise as a new perspective to vehicular networks, where the vehicles " socialize " and share common interests. In this work, we evaluate the behavior of vehicles using two mobility scenarios, in order to classify them according to the interactions performed, identifying common interests and similar routines. Thus, we use metrics of complex networks and statistical techniques. Results prove the existence of routines and human features in Vehicular Networks

Understanding Interactions in Vehicular Networks Through Taxi Mobility

International audienceVehicular Networks (VANETs) are an emerging network that enables the communication among the vehicles, in order to promote a safe and efficient traffic, avoiding crashes and preventing hazards. These vehicles transit on the streets and highways, and during their trajectories, they can communicate with each other or with another network, through interactions among them and road side units. Aiming to better understand these interactions, in this work, we characterize the vehicular mobility through a detailed analysis of dataset traces, which portray the mobility of a group of taxis in a great city. We perform the analysis using statistical techniques, graph theory and network analysis, extracting properties and behaviors from the mobility traces. The results reveal the existence of regularity and common interests in the studied traces

TruMan : trust management for vehicular networks

Orientador: Luiz Carlos Pessoa AlbiniDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 21/05/2018Inclui referências: p.54-60Área de concentração: Ciência da ComputaçãoResumo: À medida em que computadores tornam-se menores e mais poderosos, a possibilidade de integrá-los a objetos do cotidiano é cada vez mais interessante. Ao integrar processadores e unidades de comunicação sem fio a veículos, é possível criar uma rede veicular ad-hoc (VANET), na qual carros compartilham dados entre si para cooperar e criar ruas mais seguras e eficientes. Uma solução descentralizada ad-hoc, que não depende de infraestrutura pré-existente, conexão com a internet ou disponibilidade de servidores, é preferida para que a latência de entrega de mensagens seja a mais curta possível em situações críticas. No entanto, assim como é o caso de muitas novas tecnologias, VANETs serão um alvo de ataques realizados por usuários maliciosos, que podem obter benefícios ao afetar condições de trânsito. Para evitar tais ataques, uma importante característica para redes veiculares é o gerenciamento de confiança, permitindo que nós filtrem mensagens recebidas de acordo com valores de confiança previamente estabelecidos e designados a outros nós. Para gerar esses valores de confiança, nós usam informações adquiridas de interações passadas; nós que frequentemente compartilham dados falsos ou irrelevantes terão valores de confiança mais baixos do que os que aparentam ser confiáveis. Este trabalho introduz TruMan, um modelo de gerenciamento de confiança para redes veiculares no contexto de trajetos diários, utilizando o Working Day Movement Model como base para a mobilidade de nós. Este modelo de movimentação permite a comparação entre VANETs e redes sociais tradicionais, pois é possível observar que pares de veículos podem se encontrar mais de uma vez em diversos cenários: por exemplo, eles podem pertencer a vizinhos ou colegas de trabalho, ou apenas tomar rotas similares diariamente. Através de repetidos encontros, uma relação de confiança pode ser desenvolvida entre um par de nós. O valor de confiança resultante pode também ser usado para auxiliar outros nós que podem não ter uma relação desenvolvida entre si. O TruMan é baseado em um algoritmo já existente, que é desenvolvido para redes centralizadas e focado em modelos ad-hoc estáticos; seus conceitos são adaptados para servir uma rede descentralizada e dinâmica, que é o caso de VANETs. Usando valores de confiança formados por interações entre nós, um grafo de confiança é modelado; suas arestas representam as relações de confiança entre pares de nós. Então, componentes fortemente conexos do grafo são formados, de forma que cada nó em um componente confie nos outros nós do mesmo componente direta ou indiretamente. Um algoritmo de coloração de grafo é usado no grafo de componentes resultantes e, usando os resultados de coloração, é possível inferir quais nós são considerados maliciosos pelo consenso da rede. TruMan é rápido, colocando pouca carga nos computadores dos veículos, e satisfaz a maioria das propriedades desejáveis para modelos de gerenciamento de confiança veiculares. Palavras-chave: redes veiculares, gerenciamento de confiança, identificação de nós maliciosos.Abstract: As computers become small and powerful, the possibility of integrating them into everyday objects is ever more appealing. By integrating processors and wireless communication units into vehicles, it is possible to create a vehicular ad-hoc network (VANET), in which cars share data amongst themselves in order to cooperate and make roads safer and more efficient. A decentralized ad-hoc solution, which doesn't rely on previously existing infrastructure, Internet connection or server availability, is preferred so the message delivery latency is as short as possible in the case of life-critical situations. However, as is the case with most new technologies, VANETs might be a prime target for attacks performed by malicious users, who may benefit from affecting traffic conditions. In order to avoid such attacks, one important feature for vehicular networks is trust management, which allows nodes to filter incoming messages according to previously established trust values assigned to other nodes. To generate these trust values, nodes use information acquired from past interactions; nodes which frequently share false or irrelevant data will have lower trust values than the ones which appear to be reliable. This work introduces TruMan, a trust management model for vehicular networks in the context of daily commutes, utilizing the Working Day Movement Model as a basis for node mobility. This movement model allows the comparison of VANETs to traditional social networks, as it can be observed that pairs of vehicles are likely to meet more than once in several scenarios: for example, they can belong to neighbors or work colleagues, or simply take similar routes every day. Through these repeated encounters, a trust relationship can be developed between a pair of nodes. The resulting trust value can also be used to aid other nodes which might not have a developed relationship with each other. TruMan is based on a previously existing algorithm, which was developed for centralized networks and focused on static ad-hoc models; its concepts were adapted to serve a decentralized and dynamic network, which is the case of VANETs. Using trust values formed by node interactions, a trust graph is modeled; its edges represent trust relationships between pairs of nodes. Then, strongly connected components are formed so that each node in each component trusts other nodes in the same component directly or indirectly. A graph coloring algorithm is used on the resulting components graph and, using the coloring results, it is possible to infer which nodes are considered malicious by the consensus of the network. TruMan is fast, so it incurs low pressure on on-board computers, and is able to satisfy most desired properties for vehicular trust management models. Keywords: vehicular networks, trust management, malicious node identification

Is it Possible to Find Social Properties in Vehicular Networks?

International audienceEveryday, vehicles transit in a city and along their trajectories, they encounter other vehicles. The frequency of these encounters is influenced by many factors, such as: vehicle speed, destinations, traffic conditions, and the period of the day. However, these factors are justified by the public roads limits and the driver's behavior. The people present daily routines and similar behaviors that have a great impact in the daily traffic evolution. In this work, we present a numerical analysis of real and realistic data sets that describe the mobility of a set of vehicles. Social metrics are computed, and the results obtained are compared to random graphs in the direction to verify if vehicular network presents a social behavior. Finally, we discuss new social perspectives in vehicular networks