2,038 research outputs found
Quickest detection in coupled systems
This work considers the problem of quickest detection of signals in a coupled
system of N sensors, which receive continuous sequential observations from the
environment. It is assumed that the signals, which are modeled a general Ito
processes, are coupled across sensors, but that their onset times may differ
from sensor to sensor. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended average Kullback-
Leibler divergence criterion is used as a measure of detection delay, with a
constraint on the mean time between false alarms. The case in which the sensors
employ cumulative sum (CUSUM) strategies is considered, and it is proved that
the minimum of N CUSUMs is asymptotically optimal as the mean time between
false alarms increases without bound.Comment: 6 pages, 48th IEEE Conference on Decision and Control, Shanghai 2009
December 16 - 1
Quickest detection in coupled systems
This work considers the problem of quickest detection of signals in a coupled
system of sensors, which receive continuous sequential observations from
the environment. It is assumed that the signals, which are modeled by general
It\^{o} processes, are coupled across sensors, but that their onset times may
differ from sensor to sensor. Two main cases are considered; in the first one
signal strengths are the same across sensors while in the second one they
differ by a constant. The objective is the optimal detection of the first time
at which any sensor in the system receives a signal. The problem is formulated
as a stochastic optimization problem in which an extended minimal
Kullback-Leibler divergence criterion is used as a measure of detection delay,
with a constraint on the mean time to the first false alarm. The case in which
the sensors employ cumulative sum (CUSUM) strategies is considered, and it is
proved that the minimum of CUSUMs is asymptotically optimal as the mean
time to the first false alarm increases without bound. In particular, in the
case of equal signal strengths across sensors, it is seen that the difference
in detection delay of the -CUSUM stopping rule and the unknown optimal
stopping scheme tends to a constant related to the number of sensors as the
mean time to the first false alarm increases without bound. Alternatively, in
the case of unequal signal strengths, it is seen that this difference tends to
zero.Comment: 29 pages. SIAM Journal on Control and Optimization, forthcomin
Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks
Embedded sensing systems are pervasively used in life- and security-critical
systems such as those found in airplanes, automobiles, and healthcare.
Traditional security mechanisms for these sensors focus on data encryption and
other post-processing techniques, but the sensors themselves often remain
vulnerable to attacks in the physical/analog domain. If an adversary
manipulates a physical/analog signal prior to digitization, no amount of
digital security mechanisms after the fact can help. Fortunately, nature
imposes fundamental constraints on how these analog signals can behave. This
work presents PyCRA, a physical challenge-response authentication scheme
designed to protect active sensing systems against physical attacks occurring
in the analog domain. PyCRA provides security for active sensors by continually
challenging the surrounding environment via random but deliberate physical
probes. By analyzing the responses to these probes, and by using the fact that
the adversary cannot change the underlying laws of physics, we provide an
authentication mechanism that not only detects malicious attacks but provides
resilience against them. We demonstrate the effectiveness of PyCRA through
several case studies using two sensing systems: (1) magnetic sensors like those
found wheel speed sensors in robotics and automotive, and (2) commercial RFID
tags used in many security-critical applications. Finally, we outline methods
and theoretical proofs for further enhancing the resilience of PyCRA to active
attacks by means of a confusion phase---a period of low signal to noise ratio
that makes it more difficult for an attacker to correctly identify and respond
to PyCRA's physical challenges. In doing so, we evaluate both the robustness
and the limitations of PyCRA, concluding by outlining practical considerations
as well as further applications for the proposed authentication mechanism.Comment: Shorter version appeared in ACM ACM Conference on Computer and
Communications (CCS) 201
Optimal Bayesian Quickest Detection for Hidden Markov Models and Structured Generalisations
In this paper we consider the problem of quickly detecting changes in hidden
Markov models (HMMs) in a Bayesian setting, as well as several structured
generalisations including changes in statistically periodic processes, quickest
detection of a Markov process across a sensor array, quickest detection of a
moving target in a sensor network and quickest change detection (QCD) in
multistream data. Our main result establishes an optimal Bayesian HMM QCD rule
with a threshold structure. This framework and proof techniques allow us to to
elegantly establish optimal rules for several structured generalisations by
showing that these problems are special cases of the Bayesian HMM QCD problem.
We develop bounds to characterise the performance of our optimal rule and
provide an efficient method for computing the test statistic. Finally, we
examine the performance of our rule in several simulation examples and propose
a technique for calculating the optimal threshold
Bayesian Quickest Detection of Propagating Spatial Events
Rapid detection of spatial events that propagate across a sensor network is
of wide interest in many modern applications. In particular, in communications,
radar, environmental monitoring, and biosurveillance, we may observe
propagating fields or particles. In this paper, we propose Bayesian single and
multiple change-point detection procedures for the rapid detection of
propagating spatial events. It is assumed that the spatial event propagates
across a network of sensors according to the physical properties of the source
causing the event. The multi-sensor system configuration is arbitrary and
sensors may be mobile. We begin by considering a single spatial event and are
interested in detecting this event as quickly as possible, while controlling
the probability of false alarm. Using a dynamic programming framework we derive
the structure of the optimal procedure, which minimizes the average detection
delay (ADD) subject to a false alarm probability upper bound. In the rare event
regime, the optimal procedure converges to a more practical threshold test on
the posterior probability of the change point. A convenient recursive
computation of this posterior probability is derived by using the propagation
pattern of the spatial event. The ADD of the posterior probability threshold
test is analyzed in the asymptotic regime, and specific analysis is conducted
in the setting of detecting attenuating random signals. Then, we show how the
proposed procedure is easy to extend for detecting multiple propagating spatial
events in parallel. A method that provides false discovery rate (FDR) control
is proposed. In the simulation section, it is clearly demonstrated that
exploiting the spatial properties of the event decreases the ADD compared to
procedures that do not utilize this information, even under model mismatch.Comment: 14 pages, 5 figure
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