Network Intrusion Detection Using Rough Sets Based Parallel Genetic Algorithm Hybrid Model

The thesis proposes a hybrid intrusion detection model based on the parallel genetic algorithm and the rough set theory. Due to the difficult for the status of intrusion detection rules. This model, taking the advantage of rough set's streamline the edge to data and genetic algorithm's high parallelism, succeeds in introducing the genetic-rough set theory to the instrusion detection. The application of hubrid genetic algorithm in solving the rough set reduction saves computing time. The concludes that model can result in high detection rate and low false detection rate to different types of network via experiments.