43,160 research outputs found
On the Exact Solution to a Smart Grid Cyber-Security Analysis Problem
This paper considers a smart grid cyber-security problem analyzing the
vulnerabilities of electric power networks to false data attacks. The analysis
problem is related to a constrained cardinality minimization problem. The main
result shows that an relaxation technique provides an exact optimal
solution to this cardinality minimization problem. The proposed result is based
on a polyhedral combinatorics argument. It is different from well-known results
based on mutual coherence and restricted isometry property. The results are
illustrated on benchmarks including the IEEE 118-bus and 300-bus systems
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Efficient Computations of a Security Index for False Data Attacks in Power Networks
The resilience of Supervisory Control and Data Acquisition (SCADA) systems
for electric power networks for certain cyber-attacks is considered. We analyze
the vulnerability of the measurement system to false data attack on
communicated measurements. The vulnerability analysis problem is shown to be
NP-hard, meaning that unless there is no polynomial time algorithm to
analyze the vulnerability of the system. Nevertheless, we identify situations,
such as the full measurement case, where it can be solved efficiently. In such
cases, we show indeed that the problem can be cast as a generalization of the
minimum cut problem involving costly nodes. We further show that it can be
reformulated as a standard minimum cut problem (without costly nodes) on a
modified graph of proportional size. An important consequence of this result is
that our approach provides the first exact efficient algorithm for the
vulnerability analysis problem under the full measurement assumption.
Furthermore, our approach also provides an efficient heuristic algorithm for
the general NP-hard problem. Our results are illustrated by numerical studies
on benchmark systems including the IEEE 118-bus system
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
A fast approach for overcomplete sparse decomposition based on smoothed L0 norm
In this paper, a fast algorithm for overcomplete sparse decomposition, called
SL0, is proposed. The algorithm is essentially a method for obtaining sparse
solutions of underdetermined systems of linear equations, and its applications
include underdetermined Sparse Component Analysis (SCA), atomic decomposition
on overcomplete dictionaries, compressed sensing, and decoding real field
codes. Contrary to previous methods, which usually solve this problem by
minimizing the L1 norm using Linear Programming (LP) techniques, our algorithm
tries to directly minimize the L0 norm. It is experimentally shown that the
proposed algorithm is about two to three orders of magnitude faster than the
state-of-the-art interior-point LP solvers, while providing the same (or
better) accuracy.Comment: Accepted in IEEE Transactions on Signal Processing. For MATLAB codes,
see (http://ee.sharif.ir/~SLzero). File replaced, because Fig. 5 was missing
erroneousl
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