3,947 research outputs found
Location Spoofing Detection for VANETs by a Single Base Station in Rician Fading Channels
In this work we examine the performance of a Location Spoofing Detection
System (LSDS) for vehicular networks in the realistic setting of Rician fading
channels. In the LSDS, an authorized Base Station (BS) equipped with multiple
antennas utilizes channel observations to identify a malicious vehicle, also
equipped with multiple antennas, that is spoofing its location. After deriving
the optimal transmit power and the optimal directional beamformer of a
potentially malicious vehicle, robust theoretical analysis and detailed
simulations are conducted in order to determine the impact of key system
parameters on the LSDS performance. Our analysis shows how LSDS performance
increases as the Rician K-factor of the channel between the BS and legitimate
vehicles increases, or as the number of antennas at the BS or legitimate
vehicle increases. We also obtain the counter-intuitive result that the
malicious vehicle's optimal number of antennas conditioned on its optimal
directional beamformer is equal to the legitimate vehicle's number of antennas.
The results we provide here are important for the verification of location
information reported in IEEE 1609.2 safety messages.Comment: 6 pages, 5 figures, Added further clarification on constraints
imposed on the detection minimization strategy. Minor typos fixe
Enhanced Position Verification for VANETs using Subjective Logic
The integrity of messages in vehicular ad-hoc networks has been extensively
studied by the research community, resulting in the IEEE~1609.2 standard, which
provides typical integrity guarantees. However, the correctness of message
contents is still one of the main challenges of applying dependable and secure
vehicular ad-hoc networks. One important use case is the validity of position
information contained in messages: position verification mechanisms have been
proposed in the literature to provide this functionality. A more general
approach to validate such information is by applying misbehavior detection
mechanisms. In this paper, we consider misbehavior detection by enhancing two
position verification mechanisms and fusing their results in a generalized
framework using subjective logic. We conduct extensive simulations using VEINS
to study the impact of traffic density, as well as several types of attackers
and fractions of attackers on our mechanisms. The obtained results show the
proposed framework can validate position information as effectively as existing
approaches in the literature, without tailoring the framework specifically for
this use case.Comment: 7 pages, 18 figures, corrected version of a paper submitted to 2016
IEEE 84th Vehicular Technology Conference (VTC2016-Fall): revised the way an
opinion is created with eART, and re-did the experiments (uploaded here as
correction in agreement with TPC Chairs
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