655 citations · 1.4k across the 34 of their papers we have counts for
38 papers
Distributed Synchronisation of Heterogeneous Dynamical Networks With Nonlinear Diffusive Couplings
Yongkang Su, Joaquin Carrasco, Iñaki Esnaola +1
This letter investigates the problem of output synchronisation in heterogeneous dynamical networks with nonlinear diffusive couplings in the presence of disturbances on the couplin…
Empirical Risk Minimization with -Divergence Regularization
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza +1
In this paper, the solution to the empirical risk minimization problem with -divergence regularization (ERM-DR) is presented and conditions under which the solution also serv…
A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza
The dual formulation of empirical risk minimization with f-divergence regularization (ERM-fDR) is introduced. The solution of the dual optimization problem to the ERM-fDR is connec…
Decentralized Stealth Attacks on Cyber-Physical Systems
Xiuzhen Ye, Inaki Esnaola, Samir M. Perlaza +1
Decentralized stealth attack constructions that minimize the mutual information between the state variables and the measurements are proposed. The attack constructions are formulat…
Generalization Error of -Divergence Stabilized Algorithms via Duality
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza +1
The solution to empirical risk minimization with -divergence regularization (ERM-DR) is extended to constrained optimization problems, establishing conditions for equivalence…
Proofs for Folklore Theorems on the Radon-Nikodym Derivative
Yaiza Bermudez, Gaetan Bisson, Iñaki Esnaola +1
In this technical report, rigorous statements and formal proofs are presented for both foundational and advanced folklore theorems on the Radon-Nikodym derivative. The cases of con…
Stealth Attacks Against Moving Target Defense for Smart Grid
Ke Sun, Iñaki Esnaola, H. Vincent Poor
Data injection attacks (DIAs) pose a significant cybersecurity threat to the Smart Grid by enabling an attacker to compromise the integrity of data acquisition and manipulate estim…
Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza +1
The solution to empirical risk minimization with -divergence regularization (ERM-DR) is presented under mild conditions on . Under such conditions, the optimal measure is…
Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure
Xinying Zou, Samir M. Perlaza, Iñaki Esnaola +1
In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More spec…
On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation
Samir M. Perlaza, Iñaki Esnaola, Gaetan Bisson +1
The dependence on training data of the Gibbs algorithm (GA) is analytically characterized. By adopting the expected empirical risk as the performance metric, the sensitivity of the…
Analysis of the Relative Entropy Asymmetry in the Regularization of Empirical Risk Minimization
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza +1
The effect of the relative entropy asymmetry is analyzed in the empirical risk minimization with relative entropy regularization (ERM-RER) problem. A novel regularization is introd…
An information theoretic vulnerability metric for data integrity attacks on smart grids
Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza +1
A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), lever…
Empirical Risk Minimization with Relative Entropy Regularization
Samir M. Perlaza, Gaetan Bisson, Iñaki Esnaola +2
The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-RER) is investigated under the assumption that the reference measure is a -finite measure…
Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements
Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza +1
A novel metric that describes the vulnerability of the measurements in power system to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), levera…
Empirical Risk Minimization with Relative Entropy Regularization: Optimality and Sensitivity Analysis
Samir M. Perlaza, Gaetan Bisson, Iñaki Esnaola +2
The optimality and sensitivity of the empirical risk minimization problem with relative entropy regularization (ERM-RER) are investigated for the case in which the reference is a s…
Stealth Data Injection Attacks with Sparsity Constraints
Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza +1
Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the…
Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation
Ke Sun, Iñaki Esnaola, Antonia M. Tulino +1
Information-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously li…
Data-Injection Attacks
Iñaki Esnaola, Samir M. Perlaza, Ke Sun
In this chapter we review some of the basic attack constructions that exploit a stochastic description of the state variables. We pose the state estimation problem in a Bayesian se…
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based Algorithm
Jonathan Monsalve, Juan Ramirez, Iñaki Esnaola +1
Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently…
Information Theoretic Data Injection Attacks with Sparsity Constraints
Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza +1
Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimati…
Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill
Zak Hodgson, Inaki Esnaola, Bryn Jones
This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immedi…
A combined ocean and oil model for model-based adaptive monitoring
Zak Hodgson, David Browne, Inaki Esnaola +1
This paper presents a combined ocean and oil model for adaptive placement of sensors in the immediate aftermath of oilspills. A key feature of this model is the ability to correct…
Denial of Service Attacks on Control Systems with Packet Loss
William Casbolt, Iñaki Esnaola, Bryn Jones
The performance of control systems with packet loss as a result of an attack over the actuation communication channel is analysed. The operator is assumed to monitor the state of t…
Optimal Control over Multiple Input Lossy Channels
William Casbolt, Bryn Jones, Iñaki Esnaola
The performance of control systems with input packet losses on the controller to plant communication channel is analysed. The main contribution of this work is a proof that linear…