State-of-the-art intrusion detection and monitoring systems produce hundreds or even thousands of events every day. Unfortunately, most of these events are false positives, or irrelevant and can be considered as background noise, which makes their correlation, analysis and investigation very complicated and resource consuming. This paper attempts to simulate the modeling of background noise using the non-stationary time series analysis with lag smoothing Kalman filter. Then introduce and compare a second technique applying a multi-layered perceptron neural network with back  ropagation network; an approach that is used for the first time in modeling and correlating the background noise. DARPA Dataset is used to analyze and compare both techniques and finally a verification experiment is conducted using a gathered dataset from real network environment.