4,918 research outputs found
Flexible Authentication in Vehicular Ad hoc Networks
A Vehicular Ad-Hoc Network (VANET) is a form of Mobile ad-hoc network, to
provide communications among nearby vehicles and between vehicles and nearby
fixed roadside equipment. The key operation in VANETs is the broadcast of
messages. Consequently, the vehicles need to make sure that the information has
been sent by an authentic node in the network. VANETs present unique challenges
such as high node mobility, real-time constraints, scalability, gradual
deployment and privacy. No existent technique addresses all these requirements.
In particular, both inter-vehicle and vehicle-to-roadside wireless
communications present different characteristics that should be taken into
account when defining node authentication services. That is exactly what is
done in this paper, where the features of inter-vehicle and vehicle-to-roadside
communications are analyzed to propose differentiated services for node
authentication, according to privacy and efficiency needs
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
Security Analysis of Vehicular Ad Hoc Networks (VANET)
Vehicular Ad Hoc Networks (VANET) has mostly gained the attention of today's
research efforts, while current solutions to achieve secure VANET, to protect
the network from adversary and attacks still not enough, trying to reach a
satisfactory level, for the driver and manufacturer to achieve safety of life
and infotainment. The need for a robust VANET networks is strongly dependent on
their security and privacy features, which will be discussed in this paper. In
this paper a various types of security problems and challenges of VANET been
analyzed and discussed; we also discuss a set of solutions presented to solve
these challenges and problems.Comment: 6 pages; 2010 Second International Conference on Network
Applications, Protocols and Service
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
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