Network engineering for agile belief network models

Laskey, Kathryn Blackmond; Mahoney, Suzanne M.

doi:10.1109/69.868902

Cited by 60 publications

(41 citation statements)

References 17 publications

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“…These terms are borrowed from the Unified Process (UP) [Jacobson et al, 1999] with some modifications to reflect our domain of ontology modeling instead of development process. The methodology described here is also consistent with the Bayesian network modeling methodology described by [Laskey & Mahoney, 2000] and [Korb & Nicholson, 2003]. Figure 8 depicts these three stages of the Probabilistic Ontology Modeling Cycle (POMC).…”

Section: Fig 7 Uncertainty Modeling Process For the Sw (Ump-sw) Usimentioning

confidence: 49%

UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

Carvalho¹,

Laskey²,

Costa³

et al. 2010

Semantic Web

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Section: Fig 7 Uncertainty Modeling Process For the Sw (Ump-sw) Usimentioning

confidence: 49%

UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

Carvalho¹,

Laskey²,

Costa³

et al. 2010

Semantic Web

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“…Laskey K B, and Mahoney S M [19] published a paper. The construction of a large, complex belief network model, like any major system development effort, requires a structured process to manage system design and development.…”

Section: B Knowledge Engineeringmentioning

confidence: 98%

Research on Behavior Trust Based on Bayesian Inference in Trusted Computing Networks

Liu

2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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Recent years have seen explosive growth in information security problem. Trusted computing is increasingly used in the fields of information security. The basic idea of trusted computing is different from traditional security method. In other words, system abstracts normal behavior and compare with real-time behavior. Most of the current research focuses on static integrity metrics. However, it is not enough for dynamic measurement in trusted computing environment. In this paper, we present an efficient identification methodology of malicious insider user behavior. We focus on modeling insider user normal behavior in trusted computing networks. Our research methodology provides an approach to construct, assess, and optimize insider user normal behavior model. Our model framework derives from a dynamic Bayesian perspective. The experiment shows efficiency and robustness of our prototype model.

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“…The structural information encoded by an OOBN and the encapsulation of variables within an object allows the reuse of model fragments in different contexts. Similar object-oriented approaches focus on the modularization of the knowledge representation [14,22,15]. Similar to our use of transfer functions, they show how large networks, normally impractical to construct as a whole, can be woven together from smaller, more coherent and manageable components.…”

Section: Related Workmentioning

confidence: 99%

Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

Sakhanenko

Luger²

2008

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We propose a context-sensitive probabilistic modeling system (COSMOS) that reasons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information. COSMOS uses a failure-driven architecture to determine whether a context is supported, and consequently whether the current model remains applicable. The individual models are specified through sets of structured, hierarchically organized probabilistic logic statements using transfer functions that are then mapped into a representation supporting stochastic inferencing. We demonstrate COSMOS using data from a mechanical pump system.

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Network engineering for agile belief network models

Cited by 60 publications

References 17 publications

UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web

Research on Behavior Trust Based on Bayesian Inference in Trusted Computing Networks

Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

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