The Experts below are selected from a list of 16812 Experts worldwide ranked by ideXlab platform

Barbara Kordy - One of the best experts on this subject based on the ideXlab platform.

  • dag based attack and defense modeling don t miss the forest for the attack trees
    Computer Science Review, 2014
    Co-Authors: Barbara Kordy, Ludovic Pietrecambacedes, Patrick Schweitzer
    Abstract:

    This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a Hierarchical Decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to summarize the existing methodologies, compare their features, and propose a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.

  • dag based attack and defense modeling don t miss the forest for the attack trees
    arXiv: Cryptography and Security, 2013
    Co-Authors: Barbara Kordy, Ludovic Pietrecambacedes, Patrick Schweitzer
    Abstract:

    This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a Hierarchical Decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.

Patrick Schweitzer - One of the best experts on this subject based on the ideXlab platform.

  • dag based attack and defense modeling don t miss the forest for the attack trees
    Computer Science Review, 2014
    Co-Authors: Barbara Kordy, Ludovic Pietrecambacedes, Patrick Schweitzer
    Abstract:

    This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a Hierarchical Decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to summarize the existing methodologies, compare their features, and propose a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.

  • dag based attack and defense modeling don t miss the forest for the attack trees
    arXiv: Cryptography and Security, 2013
    Co-Authors: Barbara Kordy, Ludovic Pietrecambacedes, Patrick Schweitzer
    Abstract:

    This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a Hierarchical Decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements.

Degang Chen - One of the best experts on this subject based on the ideXlab platform.

  • a vector valued support vector machine model for multiclass problem
    Information Sciences, 2013
    Co-Authors: Ran Wang, Sam Kwong, Degang Chen
    Abstract:

    In this paper, a new model named Multiclass Support Vector Machines with Vector-Valued Decision (M-SVMs-VVD) or VVD is proposed. The basic idea is to separate 2^a classes by a SVM hyperplanes in the feature space induced by certain kernels, where a is a finite positive integer. We start from a 2^a-class problem, and extend it to any-class problem by applying a Hierarchical Decomposition procedure. Compared with the existing SVM-based multiclass methods, the VVD model has two advantages. First, it reduces the computational complexity by using a small number of classifiers. Second, the feature space partition induced by the hyperplanes effectively eliminates the Unclassifiable regions (URs) that may affect the classification performance of the algorithm. Experimental comparisons with several state-of-the-art multiclass methods demonstrate that VVD maintains a comparable testing accuracy, while it improves the classification efficiency with less classifiers, a smaller number of support vectors (SVs), and shorter testing time.

Michael G Strintzis - One of the best experts on this subject based on the ideXlab platform.

  • a family of wavelet based stereo image coders
    IEEE Transactions on Circuits and Systems for Video Technology, 2002
    Co-Authors: Nikolaos V. Boulgouris, Michael G Strintzis
    Abstract:

    We propose novel algorithms for stereoscopic image coding based on the Hierarchical Decomposition of stereo information. The proposed schemes, based on the wavelet transform and zerotree quantization, are endowed with excellent progressive transmission capability and retain the option for perfect reconstruction of the original image pair. Experimental evaluation shows that the resulting methods produce superior results when compared with other algorithms for stereo image coding. This is achieved without introducing blocking artifacts and with the valuable additional convenience of the production of embedded bitstreams.

  • embedded coding of stereo images
    International Conference on Image Processing, 2000
    Co-Authors: Nikolaos V. Boulgouris, Michael G Strintzis
    Abstract:

    We propose a novel algorithm for embedded stereoscopic image coding based on the Hierarchical Decomposition of stereo information. The proposed scheme is endowed with excellent progressive transmission capability and retains the option for perfect reconstruction of the original image pair. Experimental evaluation shows that the resulting method produces comparable results with a previously proposed algorithm for progressive stereo image coding.

Vassilevski, Panayot S. - One of the best experts on this subject based on the ideXlab platform.

  • Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo
    2021
    Co-Authors: Fairbanks, Hillary R., Villa Umberto, Vassilevski, Panayot S.
    Abstract:

    In this work we develop a new Hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well-suited to incorporate into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the righthand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g. multilevel Monte Carlo, the previous constructions are not directly applicable to multilevel MCMC frameworks which build fine scale random fields in a Hierarchical fashion from coarse scale random fields. Our new Hierarchical multilevel method relies on a Hierarchical Decomposition of the white noise source function in $L^2$ which allows us to form Gaussian random field realizations across multiple levels of discretization in a way that fits into multilevel MCMC algorithmic frameworks. After presenting our main theoretical results and numerical scaling results to showcase the utility of this new Hierarchical PDE method for generating Gaussian random field realizations, this method is tested on a four-level MCMC algorithm to explore its feasibility

  • Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo
    PDXScholar, 2021
    Co-Authors: Fairbanks, Hillary R., Villa, Umberto E., Vassilevski, Panayot S.
    Abstract:

    In this work we develop a new Hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable to multilevel MCMC frameworks which build fine-scale random fields in a Hierarchical fashion from coarse-scale random fields. Our new Hierarchical multilevel method relies on a Hierarchical Decomposition of the white noise source function in $L^2$ which allows us to form Gaussian random field realizations across multiple levels of discretization in a way that fits into multilevel MCMC algorithmic frameworks. After presenting our main theoretical results and numerical scaling results to showcase the utility of this new Hierarchical PDE method for generating Gaussian random field realizations, this method is tested on a four-level MCMC algorithm to explore its feasibility. Read More: https://epubs-siam-org.proxy.lib.pdx.edu/doi/abs/10.1137/20M134960