Dynamic Bayesian Networks (DBNs) bring efficient tools to model complex multivariate dynamical systems learned from collected data and/or expert knowledge. Notwithstanding, the underlying generative Markov model is supposed homogeneous; neither its topology nor its parameters evolve over time. Thus, learning a DBN to model a non-stationary process with this belief will lead to poor prediction capabilities. In order to account for nonstationary processes, we build on a framework to identify transitions between underlying models and a framework to learn them in real time, without making hypothesis about their evolution. We present the tool performances on simulated datasets. Since we aim to use this to model and predict incongruities within an Intrusion Detection System (IDS) in near real-time, great care is ascribed to the capability to correctly detect transition times. Our prior results display the precision of our algorithm in the choice of transitions and therefore the quality of identified networks. At last we suggest future work.