Model For Intrusion Detection Based Technology Of Knowledge Discovery In Database

Intrusion Detection is an effective approach to dealing with the problem of network security. One of the research emphases in the field of intrusion detection is how to analyze the received data to build a intrusion detection model with effectiveness,adaptability and extensibility. In this paper, our study focus on building intrusion detection model based the technology of Knowledge Discovery in Database(KDD). Firstly, given a sequence of temporal events a novel approach of linear temporal logic is used to identify the unexpected patterns, then offer an efficient heuristic approach that has proven experimentally effective. Secondly, a approach is provided for the construction of a Multivariate Adaptive Regression Splines(MARS) model which is used as intrusion detection models. In contrast to other heuristic approaches such as neural networks and classification trees, this approach is effectual to capture complex nonlinear relationships between the predictor and response variables. Having built MARS model, a backward pruning technique is employed to deal with the problem of the model overfitting to the training data. In the end, the effectiveness of this method is evaluated in an experiment.