The Experts below are selected from a list of 73776 Experts worldwide ranked by ideXlab platform

Alexandre Evsukoff - One of the best experts on this subject based on the ideXlab platform.

Avi Pfeffer - One of the best experts on this subject based on the ideXlab platform.

  • object oriented Bayesian Networks
    Uncertainty in Artificial Intelligence, 1997
    Co-Authors: Daphne Koller, Avi Pfeffer
    Abstract:

    Bayesian Networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian Networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies, Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN--particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts---can also be used to speed up the inference process.

Daphne Koller - One of the best experts on this subject based on the ideXlab platform.

  • object oriented Bayesian Networks
    Uncertainty in Artificial Intelligence, 1997
    Co-Authors: Daphne Koller, Avi Pfeffer
    Abstract:

    Bayesian Networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian Networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies, Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN--particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts---can also be used to speed up the inference process.

Wojciech Tylman - One of the best experts on this subject based on the ideXlab platform.

  • Misuse-based intrusion detection using Bayesian Networks
    International Journal of Critical Computer-based Systems, 2010
    Co-Authors: Wojciech Tylman
    Abstract:

    This paper presents an application of Bayesian Networks to the process of intrusion detection in computer Networks. The presented system, called Bayesian system for intrusion detection (Basset) extends functionality of Snort, an open-source network intrusion detection system (NIDS), by incorporating Bayesian Networks as additional processing stages. The flexible nature of this solution allows it to be used both for misuse-based and anomaly-based detection process; this paper concentrates on the misuse-based detection. The ultimate goal is to provide better detection capabilities and less chance of false alerts by creating a platform capable of evaluating Snort alerts in a broader context – other alerts and network traffic in general. An ability to include on-demand information from third party programmes is also an important feature of the presented approach to intrusion detection.

  • Anomaly-Based Intrusion Detection Using Bayesian Networks
    2008 Third International Conference on Dependability of Computer Systems DepCoS-RELCOMEX, 2008
    Co-Authors: Wojciech Tylman
    Abstract:

    This paper presents an application of Bayesian Networks to the process of intrusion detection in computer Networks. The presented system, called Basset (Bayesian system for intrusion detection) extends functionality of Snort, an open-source NIDS, by incorporating Bayesian Networks as additional processing stages. The flexible nature of this solution allows it to be used both for misuse-based and anomaly-based detection process; this paper concentrates on the anomaly-based detection. The ultimate goal is to create a hybrid, misuse anomaly based solution that will allow interaction between these two techniques of intrusion detection. Ability to alter its behaviour based on historical data is also an important feature of the described system.

  • Misuse-Based Intrusion Detection Using Bayesian Networks
    2008 Third International Conference on Dependability of Computer Systems DepCoS-RELCOMEX, 2008
    Co-Authors: Wojciech Tylman
    Abstract:

    This paper presents an application of Bayesian Networks to the process of intrusion detection in computer Networks. The presented system, called Basset (Bayesian system for intrusion detection) extends functionality of Snort, an open-source NIDS, by incorporating Bayesian Networks as additional processing stages. The flexible nature of this solution allows it to be used both for misuse-based and anomaly-based detection process; this paper concentrates on the misuse-based detection. The ultimate goal is to provide better detection capabilities and less chance of false alarms by creating a platform capable of evaluating Snort alerts in a broader context - other alerts and network traffic in general. An ability to include on-demand information from third party programs is also an important feature of the presented approach to intrusion detection.

Christophe Simon - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian Networks applications on dependability risk analysis and maintenance
    IFAC Proceedings Volumes, 2009
    Co-Authors: Medina G Oliva, Philippe Weber, Christophe Simon, Benoit Iung
    Abstract:

    In this paper, a bibliographical review is presented about the use of Bayesian Networks over the last decade on dependability, risk analysis and maintenance. It is shown an increasing trend of the literature and of the application of Bayesian Networks in fields related to reliability, safety and maintenance. This trend is due to the benefits that Bayesian Networks provide in contrast with other classical methods of dependability analysis such as Markov Chains and Fault Trees. Some of these benefits are: to model and to analyze complex systems, to make predictions as well as diagnostics, to compute exactly the occurrence probability of an event, to update the calculations according to evidences and to represent multimodal variables. This review is based on an extraction of 200 references; the most representative are presented.

  • Bayesian Networks inference algorithm to implement Dempster Shafer theory in reliability analysis
    Reliability Engineering and System Safety, 2008
    Co-Authors: Christophe Simon, Philippe Weber, Alexandre Evsukoff
    Abstract:

    This paper deals with the use of Bayesian Networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian Networks are identified. The basic concepts of the Bayesian Networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian Networks' inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.