519 research outputs found
Secure Sensor Design Against Undetected Infiltration: Minimum Impact-Minimum Damage
We propose a new defense mechanism against undetected infiltration into
controllers in cyber-physical systems. To this end, we cautiously design the
outputs of the sensors that monitor the state of the system. Different from the
defense mechanisms that seek to detect infiltration, the proposed approach
seeks to minimize the damage of possible attacks before they have been
detected. Controller of a cyber-physical system could have been infiltrated
into by an undetected attacker at any time of the operation. Disregarding such
a possibility and disclosing system's state without caution benefits the
attacker in his/her malicious objective. Therefore, secure sensor design can
improve the security of cyber-physical systems further when incorporated along
with other defense mechanisms. We, specifically, consider a controlled
Gauss-Markov process, where the controller could have been infiltrated into at
any time within the system's operation. In the sense of game-theoretic
hierarchical equilibrium, we provide a semi-definite programming based
algorithm to compute the optimal linear secure sensor outputs and analyze the
performance for various scenarios numerically.Comment: Submitted to the IEEE Transactions on Automatic Contro
Persuasion-based Robust Sensor Design Against Attackers with Unknown Control Objectives
In this paper, we introduce a robust sensor design framework to provide
"persuasion-based" defense in stochastic control systems against an unknown
type attacker with a control objective exclusive to its type. For effective
control, such an attacker's actions depend on its belief on the underlying
state of the system. We design a robust "linear-plus-noise" signaling strategy
to encode sensor outputs in order to shape the attacker's belief in a strategic
way and correspondingly to persuade the attacker to take actions that lead to
minimum damage with respect to the system's objective. The specific model we
adopt is a Gauss-Markov process driven by a controller with a (partially)
"unknown" malicious/benign control objective. We seek to defend against the
worst possible distribution over control objectives in a robust way under the
solution concept of Stackelberg equilibrium, where the sensor is the leader. We
show that a necessary and sufficient condition on the covariance matrix of the
posterior belief is a certain linear matrix inequality and we provide a
closed-form solution for the associated signaling strategy. This enables us to
formulate an equivalent tractable problem, indeed a semi-definite program, to
compute the robust sensor design strategies "globally" even though the original
optimization problem is non-convex and highly nonlinear. We also extend this
result to scenarios where the sensor makes noisy or partial measurements.
Finally, we analyze the ensuing performance numerically for various scenarios
Deception-As-Defense Framework for Cyber-Physical Systems
We introduce deceptive signaling framework as a new defense measure against
advanced adversaries in cyber-physical systems. In general, adversaries look
for system-related information, e.g., the underlying state of the system, in
order to learn the system dynamics and to receive useful feedback regarding the
success/failure of their actions so as to carry out their malicious task. To
this end, we craft the information that is accessible to adversaries
strategically in order to control their actions in a way that will benefit the
system, indirectly and without any explicit enforcement. Under the solution
concept of game-theoretic hierarchical equilibrium, we arrive at a
semi-definite programming problem equivalent to the infinite-dimensional
optimization problem faced by the defender while selecting the best strategy
when the information of interest is Gaussian and both sides have quadratic cost
functions. The equivalence result holds also for the scenarios where the
defender can have partial or noisy measurements or the objective of the
adversary is not known. We show the optimality of linear signaling rule within
the general class of measurable policies in communication scenarios and also
compute the optimal linear signaling rule in control scenarios
A Novel Spectrally-Efficient Scheme for Physical Layer Network Coding
In this paper, we propose a novel three-time-slot transmission scheme
combined with an efficient embedded linear channel equalization (ELCE)
technique for the Physical layer Network Coding (PNC). Our transmission scheme,
we achieve about 33% increase in the spectral efficiency over the conventional
two-time-slot scheme while maintaining the same end-toend BER performance.We
derive an exact expression for the endto- end BER of the proposed
three-time-slot transmission scheme combined with the proposed ELCE technique
for BPSK transmission. Numerical results demonstrate that the exact expression
for the end-to-end BER is consistent with the BER simulation results
On Correlation of Features Extracted by Deep Neural Networks
Redundancy in deep neural network (DNN) models has always been one of their
most intriguing and important properties. DNNs have been shown to
overparameterize, or extract a lot of redundant features. In this work, we
explore the impact of size (both width and depth), activation function, and
weight initialization on the susceptibility of deep neural network models to
extract redundant features. To estimate the number of redundant features in
each layer, all the features of a given layer are hierarchically clustered
according to their relative cosine distances in feature space and a set
threshold. It is shown that both network size and activation function are the
two most important components that foster the tendency of DNNs to extract
redundant features. The concept is illustrated using deep multilayer perceptron
and convolutional neural networks on MNIST digits recognition and CIFAR-10
dataset, respectively
Discrete-Time Polar Opinion Dynamics with Susceptibility
This paper considers a discrete-time opinion dynamics model in which each
individual's susceptibility to being influenced by others is dependent on her
current opinion. We assume that the social network has time-varying topology
and that the opinions are scalars on a continuous interval. We first propose a
general opinion dynamics model based on the DeGroot model, with a general
function to describe the functional dependence of each individual's
susceptibility on her own opinion, and show that this general model is
analogous to the Friedkin-Johnsen model, which assumes a constant
susceptibility for each individual. We then consider two specific functions in
which the individual's susceptibility depends on the \emph{polarity} of her
opinion, and provide motivating social examples. First, we consider stubborn
positives, who have reduced susceptibility if their opinions are at one end of
the interval and increased susceptibility if their opinions are at the opposite
end. A court jury is used as a motivating example. Second, we consider stubborn
neutrals, who have reduced susceptibility when their opinions are in the middle
of the spectrum, and our motivating examples are social networks discussing
established social norms or institutionalized behavior. For each specific
susceptibility model, we establish the initial and graph topology conditions in
which consensus is reached, and develop necessary and sufficient conditions on
the initial conditions for the final consensus value to be at either extreme of
the opinion interval. Simulations are provided to show the effects of the
susceptibility function when compared to the DeGroot model.Comment: Extended version, with complete proofs, of a submission to the
American Control Conference 201
Reliable Smart Road Signs
In this paper, we propose a game theoretical adversarial intervention
detection mechanism for reliable smart road signs. A future trend in
intelligent transportation systems is ``smart road signs" that incorporate
smart codes (e.g., visible at infrared) on their surface to provide more
detailed information to smart vehicles. Such smart codes make road sign
classification problem aligned with communication settings more than
conventional classification. This enables us to integrate well-established
results in communication theory, e.g., error-correction methods, into road sign
classification problem. Recently, vision-based road sign classification
algorithms have been shown to be vulnerable against (even) small scale
adversarial interventions that are imperceptible for humans. On the other hand,
smart codes constructed via error-correction methods can lead to robustness
against small scale intelligent or random perturbations on them. In the
recognition of smart road signs, however, humans are out of the loop since they
cannot see or interpret them. Therefore, there is no equivalent concept of
imperceptible perturbations in order to achieve a comparable performance with
humans. Robustness against small scale perturbations would not be sufficient
since the attacker can attack more aggressively without such a constraint.
Under a game theoretical solution concept, we seek to ensure certain measure of
guarantees against even the worst case (intelligent) attackers that can perturb
the signal even at large scale. We provide a randomized detection strategy
based on the distance between the decoder output and the received input, i.e.,
error rate. Finally, we examine the performance of the proposed scheme over
various scenarios
Evolution of Social Power in Social Networks with Dynamic Topology
The recently proposed DeGroot-Friedkin model describes the dynamical
evolution of individual social power in a social network that holds opinion
discussions on a sequence of different issues. This paper revisits that model,
and uses nonlinear contraction analysis, among other tools, to establish
several novel results. First, we show that for a social network with constant
topology, each individual's social power converges to its equilibrium value
exponentially fast, whereas previous results only concluded asymptotic
convergence. Second, when the network topology is dynamic (i.e., the relative
interaction matrix may change between any two successive issues), we show that
each individual exponentially forgets its initial social power. Specifically,
individual social power is dependent only on the dynamic network topology, and
initial (or perceived) social power is forgotten as a result of sequential
opinion discussion. Last, we provide an explicit upper bound on an individual's
social power as the number of issues discussed tends to infinity; this bound
depends only on the network topology. Simulations are provided to illustrate
our results.Comment: Extended version of submitted journal paper. Includes additional
simulation detail
Bayesian Persuasion with State-Dependent Quadratic Cost Measures
We address Bayesian persuasion between a sender and a receiver with
state-dependent quadratic cost measures for general classes of distributions.
The receiver seeks to make mean-square-error estimate of a state based on a
signal sent by the sender while the sender signals strategically in order to
control the receiver's estimate in a certain way. Such a scheme could model,
e.g., deception and privacy, problems in multi-agent systems. Existing solution
concepts are not viable since here the receiver has continuous action space. We
show that for finite state spaces, optimal signaling strategies can be computed
through an equivalent linear optimization problem over the cone of completely
positive matrices. We then establish its strong duality to a copositive
program. To exemplify the effectiveness of this equivalence result, we adopt
sequential polyhedral approximation of completely-positive cones and analyze
its performance numerically. We also quantify the approximation error for a
quantized version of a continuous distribution and show that a semi-definite
program relaxation of the equivalent problem could be a benchmark lower bound
for the sender's cost for large state spaces
Reliable Intersection Control in Non-cooperative Environments
We propose a reliable intersection control mechanism for strategic autonomous
and connected vehicles (agents) in non-cooperative environments. Each agent has
access to his/her earliest possible and desired passing times, and reports a
passing time to the intersection manager, who allocates the intersection
temporally to the agents in a First-Come-First-Serve basis. However, the agents
might have conflicting interests and can take actions strategically. To this
end, we analyze the strategic behaviors of the agents and formulate Nash
equilibria for all possible scenarios. Furthermore, among all Nash equilibria
we identify a socially optimal equilibrium that leads to a fair intersection
allocation, and correspondingly we describe a strategy-proof intersection
mechanism, which achieves reliable intersection control such that the strategic
agents do not have any incentive to misreport their passing times
strategically.Comment: Extended version (including proofs of theorems and lemmas) of the
paper: M. O. Sayin, C.-W. Lin, S. Shiraishi, and T. Basar, "Reliable
intersection control in non-cooperative environments", to appear in the
Proceedings of American Control Conference, 201
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