Abstract
In this paper, we report our recent research activities under MICIE, a European project funded under Framework-7 Programme, in which a SVM-based behavior modeling and learning algorithm is described. The proposed algorithm further exploits the adapted learning capability in SVM by using statistics analysis and K-S test verification to introduce an automated parameter control mechanism, and hence the SVM learning and detection can be made adaptive to the statistics of the input data. Experiments on telecommunication network data sets support that the proposed algorithm is able to detect undesired events effectively, presenting a good potential for development of computer-aided monitoring software tools for protection of critical infrastructures. © Springer-Verlag Berlin Heidelberg 2009.