Expert System Based Intrusion Detection System

Kernel principal component analysis (KPCA) has been effectively applied as an unsupervised non-linear feature extractor in many machine learning applications and suggested for various data stream classification tasks requiring a nonlinear transformation scheme to reduce dimensions. However, the dimensionality reduction ability is restricted because of KPCA´s high time complexity. So the practicality of KPCA on large datasets is rare. Therefore in this paper, we proposes a novel kind of incremental kernel principal component analysis algorithm: Data characteristic extraction based on IPCA algorithm-DCEIPCA, which allows efficient processing of large datasets and overcome the insufficient of KPCA. On the basis of DCEIPCA, we propose Classification expert system (CES) for intrusion detection system. Extensive experiments on KDDcup99 datasets confirm the superiority of Intrusion detection system based on CES (IDSCES) over other recent Intrusion detection system^[4-11].