0000000000529161

AUTHOR

Roberto Lopez-valcarce

showing 3 related works from this author

Locally optimal invariant detector for testing equality of two power spectral densities

2018

This work addresses the problem of determining whether two multivariate random time series have the same power spectral density (PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate case, we prove that the LMPIT do…

Multivariate statisticsSeries (mathematics)Computer scienceGaussianDetectorUnivariateSpectral density020206 networking & telecommunications02 engineering and technologyUniformly most powerful invariant test (UMPIT)01 natural sciencesMatrix decomposition010104 statistics & probabilitysymbols.namesakePower spectral density (PSD)0202 electrical engineering electronic engineering information engineeringsymbols0101 mathematicsInvariant (mathematics)Time seriesHypothesis testGeneralized likelihood ratio test (GLRT)AlgorithmLocally most powerful invariant test (LMPIT)Statistical hypothesis testing
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Defending Surveillance Sensor Networks Against Data-Injection Attacks Via Trusted Nodes

2018

By injecting false data through compromised sensors, an adversary can drive the probability of detection in a sensor network-based spatial field surveillance system to arbitrarily low values. As a countermeasure, a small subset of sensors may be secured. Leveraging the theory of Matched Subspace Detection, we propose and evaluate several detectors that add robustness to attacks when such trusted nodes are available. Our results reveal the performance-security tradeoff of these schemes and can be used to determine the number of trusted nodes required for a given performance target.

business.industryComputer scienceDetector020206 networking & telecommunications020207 software engineering02 engineering and technologyAdversaryRobustness (computer science)Injection attacks0202 electrical engineering electronic engineering information engineeringbusinessWireless sensor networkSubspace topologyComputer Science::Cryptography and SecurityComputer network
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Testing Equality of Multiple Power Spectral Density Matrices

2018

This paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the c…

Multivariate statisticsGaussian02 engineering and technologyGeneralized likelihood tatio test (GLRT)Toeplitz matrixUniformly most powerful invariant test (UMPIT)01 natural sciencesElectronic mail010104 statistics & probabilitysymbols.namesakePower spectral density (PSD)0202 electrical engineering electronic engineering information engineeringApplied mathematics0101 mathematicsElectrical and Electronic EngineeringGeneralized likelihood ratio test (GLRT)MathematicsTelecomunicaciones1299 Otras Especialidades MatemáticasDetectorUnivariateSpectral density020206 networking & telecommunicationsInvariant (physics)Toeplitz matrixSignal ProcessingsymbolsTime-SeriesLocally most powerful invariant test (LMPIT)
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