An intrusion detection classification model base on projection pursuit

Intrusion detection is a challenging and critical problem in network security. Extensive research activities have been aimed at network-based intrusion detection systems, but most of them are proved unsatisfactory. This paper presents an effective intrusion detection classification model based on projection pursuit. With maximizing a projection index, Projection Pursuit uses Genetic Algorithm to search for the optimal projection direction, projects network connections records from high-dimensional space into 1-dimensional space, and uses the projection values to analyze the data structure and classify the type of intrusion. This intrusion detection classification model not only cuts down the computing complexity in the process of network connection records, but also opens out non-linear structure not like in latent semantics analysis only discovering linear structure, and the results of classification can also be visualized. The results of the experiments on KDD CUP 1999 data show that this model not only has good performance, but also reduces the number of false alarms effectively.