Dynamic Bayesian Networks (DBNs) provide a principled scheme formodeling and learning conditional dependencies from complex multivariate time-series data and have been used in a wide scope. However, in most cases, the un-derlying generative Markov model is assumed to be homogeneous, meaning thatneither its topology nor its parameters evolve over time. Therefore, learning aDBN to model a non-stationary process under this assumption will amount topoor predictions capabilities. To account for non-stationary processes, we buildon a framework to identify, in a streamed manner, transition times between un-derlying models and a framework to learn them in real time, without assumptionsabout their evolution. We show the method performances on simulated datasets.The goal of the system is to model and predict incongruities for an Intrusion Dec-tection System (IDS) in near real-time, so great care is attached to the ability tocorrectly identify transitions times. Our preliminary results reveal the precisionof our algorithm in the choice of transitions and consequently the quality of thediscovered networks. We finally suggest future works.
ISSN: 1865-0929 Communications in Computer and Information Science http://hal.upmc.fr/hal-01329583 Communications in Computer and Information Science, Springer Verlag, 2016, Information Processing and Management of Uncertainty in Knowledge-Based Systems, 610 - 611ARRAY(0x7f5470902238) 2016-07-18