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Here we develop an integrated tool for the online detection of network anomalies. We consider statistical change point detection algorithms, for both local changes in the variance and for the detection of jumps, and propose modified versions of these algorithms based on moving window techniques. We investigate performances on simulated data and on network traffic data with several superimposed attacks. All detection methods are based on wavelet packet transforms.

This article considers linear regression models with long memory errors. These models have been proven useful for application in many areas, such as medical imaging, signal processing, and econometrics. Wavelets, being self-similar, have a strong connection to long memory data. Here we employ discrete wavelet transforms as whitening filters to simplify the dense variance-covariance matrix of the data. We then adopt a Bayesian approach for the estimation of the model parameters. Our inferential procedure uses exact wavelet coefficients variances and leads to accurate estimates of the model parameters. We explore performances on simulated data and present an application to an fMRI data set. In the application we produce posterior probability maps (PPMs) that aid interpretation by identifying voxels that are likely activated with a given confidence.

a b s t r a c tPrescribed fire can release herbaceous forages from woody plant competition thus promoting increased forage plant production, vigor, and accessibility. Prescribe fire also consumes standing litter thereby improving forage quality and palatability. Consequently, prescribed fire is commonly considered an effective tool for manipulating livestock distribution on rangelands. Efficacy of this tool on mesic sagebrush steppe, however, has received little research attention. Beginning in 2001, resource selection by beef cows under a mid-summer (July) grazing regime was evaluated using global positioning system (GPS) collars for 2 years prior to and for up to 5 years after a fall prescribed fire was conducted on mesic sagebrush steppe in the Owyhee Mountains of southwestern Idaho, USA. Cattle selected for burned areas during the first, second, and fifth postfire years. Cattle had exhibited neutral selectivity towards these areas, during one of the two prefire years. Burning in the uplands reduced cattle use of near-stream habitats but only during the second postfire year. Differences in phenological timing of grazing may account for differences in cattle response to burning noted between this study and one conducted nearby under a spring (May) grazing regime. This is a case study and caution should be taken in extrapolating these results.Published by Elsevier Ltd.