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Creators/Authors contains: "Kegelmeyer, W. Philip"
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  8. This report is the final summation of Sandia's Grand Challenge LDRD project No.119351, 'Network Discovery, Characterization and Prediction' (the 'NGC') which ran from FY08 to FY10. The aim of the NGC, in a nutshell, was to research, develop, and evaluate relevant analysis capabilities that address adversarial networks. Unlike some Grand Challenge efforts, that ambition created cultural subgoals, as well as technical and programmatic ones, as the insistence on 'relevancy' required that the Sandia informatics research communities and the analyst user communities come to appreciate each others needs and capabilities in a very deep and concrete way. The NGC generated amore » number of technical, programmatic, and cultural advances, detailed in this report. There were new algorithmic insights and research that resulted in fifty-three refereed publications and presentations; this report concludes with an abstract-annotated bibliography pointing to them all. The NGC generated three substantial prototypes that not only achieved their intended goals of testing our algorithmic integration, but which also served as vehicles for customer education and program development. The NGC, as intended, has catalyzed future work in this domain; by the end it had already brought in, in new funding, as much funding as had been invested in it. Finally, the NGC knit together previously disparate research staff and user expertise in a fashion that not only addressed our immediate research goals, but which promises to have created an enduring cultural legacy of mutual understanding, in service of Sandia's national security responsibilities in cybersecurity and counter proliferation.« less
  9. A key assumption in supervised machine learning is that future data will be similar to historical data. This assumption is often false in real world applications, and as a result, prediction models often return predictions that are extrapolations. We compare four approaches to estimating extrapolation risk for machine learning predictions. Two builtin methods use information available from the classification model to decide if the model would be extrapolating for an input data point. The other two build auxiliary models to supplement the classification model and explicitly model extrapolation risk. Experiments with synthetic and real data sets show that the auxiliarymore » models are more reliable risk detectors. To best safeguard against extrapolating predictions, however, we recommend combining builtin and auxiliary diagnostics.« less
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