Emilia Tantar

University of Luxembourg · Interdisciplinary Centre for Security, Reliability and Trust

Through this position paper we aim at providing a prototype cognitive security service for anomaly detection in Software Defined Networks (SDNs). We equally look at strengthening attack detection capabilities in SDNs, through the addition of predictive analytics capabilities. For this purpose, we build a learning-based anomaly detection service cal...

This book comprises selected research papers from the 2015 edition of the EVOLVE conference, which was held on June 18–June 24, 2015 in Iași, Romania. It presents the latest research on Probability, Set Oriented Numerics, and Evolutionary Computation. The aim of the EVOLVE conference was to provide a bridge between probability, set oriented numeric...

This work presents an asymmetric quadratic approximation model and an ε-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The ε-archiving algorithm is designed to maintain and update a...

The rise of the data centers industry, together with the emergence of large cloud computing that require large quantities of resources to be maintained, brought the need of providing a sustainable development process. Through this paper we aim to provide an introductory insight on the status and tools available to tackle this perspective within the...

This paper describes the latest communications technologies emphasizing the need of dynamic network control and real-time management operations. It is advocated that many such operations can profit from cognitive learning based techniques that could drive many management or control operations. In that context a short overview of selected networking...

Security is one of the most important requirements for networks and serious concerns for network providers and users. Software Defined Networking offers to network managers new opportunities for deploying efficient security mechanisms. By means of applications and controller functionalities, SDN is able to provide a highly reactive network security...

The aim of this book is to provide a strong theoretical support for understanding and analyzing the behavior of evolutionary algorithms, as well as for creating a bridge between probability, set-oriented numerics and evolutionary computation. The volume encloses a collection of contributions that were presented at the EVOLVE 2011 international work...

Genetic type particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics, information theory, and engineering sciences. Understanding rigorously these new Monte Carlo simulation tools leads to fascinating mathematics related to...

The present study aims at investigating advanced subset simulation techniques, which are based on the theory of particle filter, for the assessment of the failure probability of a marine structure under extreme loading conditions. Three approaches are considered, namely the classical particle filter method, the subset simulation with a branching pr...

The interest in sparse antenna arrays is growing, mainly due to cost concerns, array size limitations, etc. Formally, it can be shown that their design can be expressed as a constrained multidimensional nonlinear optimization problem. Generally, through lack of convex property, such a multiextrema problem is very tricky to solve by usual determinis...

The herein paper addresses the issue of providing a model and guidelines for constructing a sustainable ICT environment at the University of Luxembourg. A particular context is thus considered, based on a real-life project that has as aim to provide a sustainable environment for the ICT infrastructure of the university. According to the different e...

In this work we focus on defining how dynamism can be modeled in the context of multi-objective optimization. Based on this, we construct a component oriented classification for dynamic multi-objective optimization problems. For each category we provide synthetic examples that depict in a more explicit way the defined model. We do this either by po...

In this work we provide a formal model for the different time-dependent components that can appear in dynamic multi-objective optimization problems, along with a classification of these components. Four main classes are identified, corresponding to the influence of the parameters, objective functions, previous states of the dynamic system and, last...

This work deals with the computation and the selection of approximate – or ε-efficient – solutions of {0, 1}-knapsack problems. By allowing approximate solutions in general a much larger variety of possibilities for the underlying problem is offered to the decision maker. We enlighten the gap that can occur when passing ε-approximate solutions from...

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though b...

An ensemble of local search algorithms are discussed and analyzed, having different initial landscape perspectives as starting point – the conformational sampling problem is considered as a case study. Grid enabled hierarchical and multistage distributed evolutionary algorithms have been addressed in previous studies, combining complementary techni...

In this work we study the convergence of generic stochastic search algorithms toward the entire set of approximate solutions of continuous multi-objective optimization problems. Since the dimension of the set of interest is typically equal to the dimension of the parameter space, we focus on obtaining a finite and tight approximation, measured by t...

The integration of information provided by an a priori landscape analysis as a guiding tool for interactive EMO methods is proposed. For this purpose, a new type of a priori landscape analysis is introduced, namely ellipse enclosure of the feasible solutions set in the solution space. The interaction takes place in the solution space, the user havi...

Recently, a framework for the approximation of the entire set of $\epsilon$-efficient solutions (denote by $E_\epsilon$) of a multi-objective optimization problem with stochastic search algorithms has been proposed. It was proven that such an algorithm produces -- under mild assumptions on the process to generate new candidate solutions --a sequenc...

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though b...