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BAYESIAN BELIEF NETWORKS (2) answer(s).
 
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ID:   181781


Estimating nuclear proliferation and security risks in emerging markets using Bayesian Belief Networks / Carless, Travis S   Journal Article
Carless, Travis S Journal Article
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Summary/Abstract An estimated 28 countries are interested in introducing nuclear power into their electric grid mix. The sudden influx of new nuclear power plants into emerging nuclear energy countries can present further nuclear proliferation and security risks. These risks can be even more prevalent for nations with political instability and limited resources to adequately support a robust nuclear regulatory infrastructure. This paper estimates the nuclear proliferation and security risks associated with the deployment of Generation III + nuclear power plants and Small Modular Reactors to emerging nuclear energy countries using expert judgment in conjunction with Bayesian Belief Networks. On average, Turkey is the most likely to divert nuclear material to develop a nuclear weapon (46% with an rsd of 0.50), divert civilian nuclear knowledge and technology for military use (38% with an rsd of 0.61), and to have their nuclear material stolen by non-state actors (39% with an rsd of 0.65). This is followed by Saudi Arabia at 38% (0.66 rsd), 39% (0.64 rsd), 32% (0.83 rsd), respectively. Reactor type has minimal impact on risk, while nations that pursue domestic enrichment and reprocessing has the greatest impact. In scenarios where emerging nuclear energy countries pursue domestic enrichment and reprocessing, the nuclear proliferation and security risks increase between 16% and 18%, on average. Lower-risk countries that engage in domestic enrichment and reprocessing can have comparable nuclear proliferation and security risks as Turkey and Saudi Arabia.
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2
ID:   177282


Modeling Attribution of Cyber Attacks Using Bayesian Belief Networks / Sharma, Munish   Journal Article
Sharma, Munish Journal Article
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Summary/Abstract The article makes an attempt to bring to the fore the various factors which are considered in the due process of attribution of a cyber-attack and the correlation of credible attribution with cyber deterrence. The focal point of the article is a three-step approach to model the decision-making process behind attribution of cyber-attacks using Bayesian Belief Networks and a case study to elucidate on the functioning of the model. Bayesian Belief Networks represent relationships between variables or multiple events and they are used to draw inference or estimate the probability to help decision-making under the conditions of uncertainty.
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