Information security is an important and growing need. The most common schemes used for detection systems include pattern- or signature-based and anomaly-based. Anomaly-based schemes use a set of metrics, which outline the normal system behavior and

A multimodal function is a function with more than one optimum. This work proposes a method for automatic determination of regions of the search space of a given function. Those regions guarantee enclose one an optimum. Based on an initial sampling of the search space, an incremental convex hull algorithm is used to incrementally grow regions that enclose one optimum. After applying the method we can use any optimization algorithm on each of the determined region; comparing with genetic algorithms for multimodal functions, the proposed method eliminates the use of parameters like radius in fitness sharing.

Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans. It has several salient features; namely, the following. 1) Instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. 2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from nonbrain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. 3) Finally, a novel algorithm is presented which is a variant of the expectation-maximization procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.

We present a new algorithm for the efficient estimation of piecewise parametric models for image segmentation. This algorithm permits the simultaneous estimation of: the number of models; the parameters for each model and the regions where each model is applicable. It is based on Bayesian estimation theory, and is theoretically justified by the use of a specific cost function that decreases at every iteration and by a new model for the posterior marginal distributions which is amenable to the use of fast computational methods.