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NetGlyphizer: Labeled Network Traffic Generation Using Representation Learning and Transformers
No full textNational audienceNetwork security has become increasingly critical due to the growing frequency and sophistication of cyberattacks. To enhance intrusion detection capabilities, emerging solutions leverage machine learning (ML) models. However, a significant challenge persists in acquiring sufficient high-quality training data. To overcome this challenge, we explored the use of generative neural networks to create realistic and synthetic network traffic data. This paper introduces NetGlyphizer, a novel approach to learn a discrete representation of network traffic using Vector Quantized-Variational Autoencoders (VQ-VAE). This model transforms network flows into a sequence of discrete tokens, referred to as NetGlyphs. This method enables the use of Transformer models to generate new NetGlyphs sequences, which can be decoded into real network traffic. The efficacy of this approach is evaluated using a dataset comprising both benign and malicious traffic flows. In comparison to a method employing a continuous representation, our model exhibits superior performances in accurately reconstructing the data and preserving the original distribution. Additionally, conditional generation facilitates the generation of labeled network traffic flows based on the specific network traffic class. The results demonstrate that our approach effectively preserves protocol compliances and usages, making it a promising solution for labeled network traffic generation
LMI results using IQCs and projections for the heat equation coupled to ODEs
No full textThis paper investigates the stability of a coupled system consisting of a finite-dimensional ordinary differential equation (ODE) and a partial differential equation (PDE), with a focus on incorporating boundary condition derivatives into the stability analysis. Building on a combination of projection methods and Integral Quadratic Constraints (IQCs), we develop a novel approach that generates stability conditions through Linear Matrix Inequalities (LMIs). The IQC framework rigorously accounts for the interconnection structure and dissipation mechanisms at the boundaries, providing a more comprehensive analysis of coupled finite and infinite-dimensional systems while explicitly considering boundary condition dynamics and their impact on stability
A Linear Complementarity based MPC for Aerial Physical Interaction
No full textInternational audienceThis paper presents a general MPC-based control framework that includes the linear complementarity problem (LCP) for modeling the interaction forces of a mobile robot. To validate our approach, two case studies are considered: (i) an aerial robot that should reach a target point placed on a frictionless surface; and (ii) an aerial robot that should lift a cable-suspended mass, switching from a slack to a taut cable condition. The simulation results confirm the validity of our approach, and the ability of the LCP to model the interaction forces for an aerial platform
Automated growth of AlAs/GaAs Bragg mirror with real-time feedback reflectometry.
No full textInternational audienc
Fast control allocation algorithm for tilt-rotor VTOL aircraft
No full textControl algorithms initially developed for tilt-wing vertical take-off and landing (VTOL) aircraft are adapted to the tilt-rotor design. The main difference between the two types of planes is the more complicated interaction between propellers and wings in the tilt-rotor design. Unlike tilt-wing design, the tilt-rotor case varies the angle between the propeller disk and wing cord line, thus introducing a non-linear dependency of lift on thrust and tilt angle. In this paper we develop a precise control allocation method, utilizing Groebner basis algorithms to mask the non-linearity of the control action and allow the use of linear time-invariant control laws for attitude and velocity control architectures. The performance of our approach is discussed and quantified w.r.t. the accuracy of the developed propeller-wing interaction model
AFflecto: A web server to generate conformational ensembles of flexible proteins from AlphaFold models
No full textInternational audienceIntrinsically disordered proteins and regions (IDPs/IDRs) leverage their structural flexibility to fulfill essential cellular functions, with dysfunctions often linked to severe diseases. However, the relationships between their sequences, structural dynamics and functional roles remain poorly understood. Understanding these complex relationships is crucial for therapeutic development, highlighting the need for methods to generate plausible IDP/IDR conformational ensembles. While AlphaFold (AF) excels at modeling structured domains, it fails to accurately represent disordered regions, leaving a significant portion of proteomes inaccurately modeled. We present AFflecto, a user-friendly web server for generating large conformational ensembles of proteins that include both structured domains and IDRs from AF structural models. AFflecto identifies IDRs as tails, linkers or loops by analyzing their structural context. Additionally, it incorporates a method to identify conditionally folded IDRs that AF may incorrectly predict as natively folded elements. The conformational space is globally explored using efficient stochastic sampling algorithms. AFflecto's web interface allows users to customize the modeling, by modifying boundaries between ordered and disordered regions, and selecting among several sampling strategies. The web server is freely available at https://moma.laas.fr/applications/AFflecto/
Polynomial Optimization for Nonlinear Dynamics: Theory, Algorithms and Applications
No full textInternational audienceThis workshop focused on using computational tools of polynomial optimization to deduce information about nonlinear dynamical systems, including systems governed by ordinary or partial differential equations. This approach sits at the interface of various research areas, requiring combinations of applied nonlinear dynamics and control theory, polynomial optimization, real algebraic geometry, partial differential equations, and variational analysis. The workshop brought together researchers in these different areas to share recent advances and to build the connections required for further progress
Converse Lyapunov Results for Stability of Switched Systems with Average Dwell-Time
No full textThis article provides a characterization of stability for switched nonlinear systems under average dwell-time constraints, in terms of necessary and sufficient conditions involving multiple Lyapunov functions. Earlier converse results focus on switched systems with dwell-time constraints only, and the resulting inequalities depend on the flow of individual subsystems. With the help of a counterexample, we show that a lower bound that guarantees stability for dwell-time switching signals may not necessarily imply stability for switching signals with same lower bound on the average dwell-time. Based on these two observations, we provide a converse result for the average dwell-time constrained systems in terms of inequalities which do not depend on the flow of individual subsystems and are easier to check. The particular case of linear switched systems is studied as a corollary to our main result
An Immersed Boundary Method for pressure-based compressible solvers with applications to free-convection flows, acoustic wave propagation and thermal plasma
No full textInternational audienceImmersed Boundary Methods (IBM) are a practical class of methods that enable fluid computations in complex geometry while keeping a structured mesh. Most of the existing IBM have been developed in the framework of incompressible solvers, despite their significant interest to perform simulations in more complex configurations requiring a compressible solver. In the last years, pressure-based solvers met a growing interest to perform numerical simulations of compressible flows, due to their attractive features, as removing the stability condition on the acoustic time step, and being asymptotically preserving of the incompressible regime when the Mach number tends to zero. As this class of compressible solvers share many common features with classical projection methods for incompressible flows, our objective in this paper is to present an adaptation of an efficient and accurate IBM developed for an incompressible solver by Ng et al. in [1] to a pressure-based compressible solver recently published by Urbano et al. in [2]. The proposed algorithm benefits of the attractive properties of the original IBM proposed in [1] while being able to undertake simulations in much more complex configurations. In particular, we will present validations and illustrations of the proposed solver in various configurations as free-convection flows, acoustic waves propagating in a variable section pipe or interacting with a solid obstacle, as well as the description of a thermal plasma during an electric arc discharge in a gas
Rank conditions for exactness of semidefinite relaxations in polynomial optimization
No full textInternational audienceWe consider the Moment-SOS hierarchy in polynomial optimization. We first provide a sufficient condition to solve the truncated K-moment problem associated with a given degree-2n pseudo-moment sequence φ n and a semi-algebraic set K ⊂ R d . Namely, let 2v be the maximum degree of the polynomials that describe K. If the rank r of its associated moment matrix is less than nv + 1, then φ n has an atomic representing measure supported on at most r points of K. When used at step-n of the Moment-SOS hierarchy, it provides a sufficient condition to guarantee its finite convergence (i.e., the optimal value of the corresponding degree-n semidefinite relaxation of the hierarchy is the global minimum). For Quadratic Constrained Quadratic Problems (QCQPs) one may also recover global minimizers from the optimal pseudo-moment sequence. Our condition is in the spirit of Blekherman's rank condition and while on the one-hand it is more restrictive, on the other hand it applies to constrained POPs as it provides a localization on K for the representing measure