We present an adaptive end-host anomaly detector where a supervised classifier trained as a traffic predictor is used to control a time-varying detection threshold. Using real enterprise traffic traces for both training and testing, we show that our detector outperforms a fixed-threshold detector. This comparison is robust to the choice of off-theshelf classifier and to a variety of performance...

The rapid development in the area of cognitive radio technology leads the society to higher standards of spectrum sensing performance, particularly in low signal-to-noise ratio (SNR) environment. This article proposes an adaptive double-threshold energy sensing method based on Markov model (ADEMM). When using the double-threshold energy sensing method, the modified Markov model that accounts fo...

The threshold signature scheme is a protocol that allows any subset of t parties out of n to generate a signature. Since the t members can cooperate together to compute the secret key, we introduce the server-aided threshold signature, which provides controllability for activating the signing function in a certain enhanced way. In this study, we present a server-aided threshold RSA signature pr...

Energy sense of adaptive double threshold joint spectrum perception algorithm using multiple cognitive, and after double threshold for comprehensive judgment, and threshold value with the discretion of the signal to noise ratio (SNR) adaptive adjustment, will eventually each SU decision data fusion center for spectrum using the information, make perception performance effectively improve. In th...

Edge detection is the most important task in image processing. It detects the presence and location of edges from an image having sharp changes in brightness or color intensity of an image. There are various techniques present for edge detection but the newer technique, Ant Colony Optimization is introduced here. Primarily ACO is used to detect edges of ray scale image but here an attempt is ma...

The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein proposed to use for image binarization an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that high image gradient indicates probable object edges, and there the image values...

Bayesian adaptive threshold procedures may be run for a fixed number of trials, or may be stopped when the calculated confidence interval for the threshold reaches a selected limit (a dynamic termination criterion). This study used Monte-Carlo simulations to determine whether the confidence interval is a useful predictor of errors in the estimated threshold. No difference was found between the ...

One class of adaptive psychophysical procedures was studied, using simulated and human observers. These procedures are those which require an increase in stimulus intensity after an incorrect response, and a decrease after k successive correct responses. This paper analyzes how step size and the value of k affect the mean and standard deviation of threshold estimates based on a k-down 1-up adap...

The local polynomial approximation (LPA) of noisy data is considered with the new adaptive procedure for varying bandwidth selection. The algorithm is simple to implement and nearly optimal within In N factor in the point-wise risk for estimating the function and its derivatives. The adaptive varying bandwidth enables the algorithm to be spatial adaptive over a wide range of the classes of func...

Neurons spike when their membrane potential exceeds a threshold value. In central neurons, the spike threshold is not constant but depends on the stimulation. Thus, input-output properties of neurons depend both on the effect of presynaptic spikes on the membrane potential and on the dynamics of the spike threshold. Among the possible mechanisms that may modulate the threshold, one strong candi...