8,649 research outputs found
Automatic analysis of distance bounding protocols
Distance bounding protocols are used by nodes in wireless networks to
calculate upper bounds on their distances to other nodes. However, dishonest
nodes in the network can turn the calculations both illegitimate and inaccurate
when they participate in protocol executions. It is important to analyze
protocols for the possibility of such violations. Past efforts to analyze
distance bounding protocols have only been manual. However, automated
approaches are important since they are quite likely to find flaws that manual
approaches cannot, as witnessed in literature for analysis pertaining to key
establishment protocols. In this paper, we use the constraint solver tool to
automatically analyze distance bounding protocols. We first formulate a new
trace property called Secure Distance Bounding (SDB) that protocol executions
must satisfy. We then classify the scenarios in which these protocols can
operate considering the (dis)honesty of nodes and location of the attacker in
the network. Finally, we extend the constraint solver so that it can be used to
test protocols for violations of SDB in these scenarios and illustrate our
technique on some published protocols.Comment: 22 pages, Appeared in Foundations of Computer Security, (Affiliated
workshop of LICS 2009, Los Angeles, CA)
On the Security of the Automatic Dependent Surveillance-Broadcast Protocol
Automatic dependent surveillance-broadcast (ADS-B) is the communications
protocol currently being rolled out as part of next generation air
transportation systems. As the heart of modern air traffic control, it will
play an essential role in the protection of two billion passengers per year,
besides being crucial to many other interest groups in aviation. The inherent
lack of security measures in the ADS-B protocol has long been a topic in both
the aviation circles and in the academic community. Due to recently published
proof-of-concept attacks, the topic is becoming ever more pressing, especially
with the deadline for mandatory implementation in most airspaces fast
approaching.
This survey first summarizes the attacks and problems that have been reported
in relation to ADS-B security. Thereafter, it surveys both the theoretical and
practical efforts which have been previously conducted concerning these issues,
including possible countermeasures. In addition, the survey seeks to go beyond
the current state of the art and gives a detailed assessment of security
measures which have been developed more generally for related wireless networks
such as sensor networks and vehicular ad hoc networks, including a taxonomy of
all considered approaches.Comment: Survey, 22 Pages, 21 Figure
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art
performance for automatic medical image segmentation. However, they have not
demonstrated sufficiently accurate and robust results for clinical use. In
addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability to previously unseen object classes. To address these
problems, we propose a novel deep learning-based framework for interactive
segmentation by incorporating CNNs into a bounding box and scribble-based
segmentation pipeline. We propose image-specific fine-tuning to make a CNN
model adaptive to a specific test image, which can be either unsupervised
(without additional user interactions) or supervised (with additional
scribbles). We also propose a weighted loss function considering network and
interaction-based uncertainty for the fine-tuning. We applied this framework to
two applications: 2D segmentation of multiple organs from fetal MR slices,
where only two types of these organs were annotated for training; and 3D
segmentation of brain tumor core (excluding edema) and whole brain tumor
(including edema) from different MR sequences, where only tumor cores in one MR
sequence were annotated for training. Experimental results show that 1) our
model is more robust to segment previously unseen objects than state-of-the-art
CNNs; 2) image-specific fine-tuning with the proposed weighted loss function
significantly improves segmentation accuracy; and 3) our method leads to
accurate results with fewer user interactions and less user time than
traditional interactive segmentation methods.Comment: 11 pages, 11 figure
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
We present an automatic method to describe clinically useful information
about scanning, and to guide image interpretation in ultrasound (US) videos of
the fetal heart. Our method is able to jointly predict the visibility, viewing
plane, location and orientation of the fetal heart at the frame level. The
contributions of the paper are three-fold: (i) a convolutional neural network
architecture is developed for a multi-task prediction, which is computed by
sliding a 3x3 window spatially through convolutional maps. (ii) an anchor
mechanism and Intersection over Union (IoU) loss are applied for improving
localization accuracy. (iii) a recurrent architecture is designed to
recursively compute regional convolutional features temporally over sequential
frames, allowing each prediction to be conditioned on the whole video. This
results in a spatial-temporal model that precisely describes detailed heart
parameters in challenging US videos. We report results on a real-world clinical
dataset, where our method achieves performance on par with expert annotations.Comment: To appear in MICCAI, 201
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