This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks. SVM methods are seen as competitive with benchmark methods and other studies, and are used as a standard for the anomaly detection investigation. The anomaly detection approaches compare one class SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA/KDD-99 publicly available dataset of features from network packets, classified into nonattack and four-attack categories.

This paper describes a new approach for representing, recognizing and interpreting human activity from video. The approach presented (at the conceptual level) is a model based on the hierarchical synergy of three other models (the L-G graph, the SPN graph and a NN model). In particular, in our project human activity is strongly related with the ability of describing and interrelating events. Thus, the L-G graph (local-global graph) provides a powerful description of the structural image features presented in an event, the SPN (stochastic Petri net) model offers a description of the functional behavior of the changes or operations in video presented in an event, and the NN (neural network) model provides the capability of extracting and learning behavioral patterns, presented in human activities.

A system for interpretation of images of paper-based line drawings is described. Since a typical drawing contains both text strings and graphics, an algorithm has been developed to locate and separate text strings of various font sizes, styles, and orientations. This is accomplished by applying the Hough transform to the centroids of connected components in the image. The graphics in the segmented image are processed to represent thin entities by their core-lines and thick objects by their boundaries. The core-lines and boundaries are segmented into straight line segments and curved lines. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Such a description includes the location and attributes of simple polygonal shapes, circles, and interconnecting lines, and a description of the spatial relationships and occlusions among them. Hatching and filling patterns are also identified. The performance of the system is evaluated using several test images, and the results are presented. The superiority of these algorithms in generating meaningful interpretations of graphics, compared to conventional data compression schemes, is clear from these results.>

A system for automatic recognition and description of graphical primitives and their interconnections in a mixed text/graphics image is described. The input to the system is a high-resolution binary image obtained by scanning paper-based documents. The Hough transform is applied to the connected components of the image to locate and separate text strings of various font size and orientation. The graphics in the segmented image is processed to represent thin entities by their core lines and thick objects by their boundaries. Lines of various types are identified and segmented into straight line and curved line segments during this process. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Various hatching and filling patterns in the image are identified and described. The algorithm has been implemented as software, and its robustness and performance have evaluated tested using a number of test images. The results for a simple test image are reported.>