Expert System Committee Based Intrusion Detection System
Kernel principal component analysis (KPCA) ,which is a feature extraction method and require a nonlinear transformation scheme to reduce dimensions,has been suggested for various real time classification tasks. However,the dimensionality reduction ability is restricted because of its high complexity. Therefore this paper proposes an incremental kernel principal component analysis algorithm: Data characteristic extraction based on IPCA algorithm -DCEIPCA,which can overcome the insufficient of KPCA On the basis of DCEIPCA,we propose Classification expert system committee (CESC) for intrusion detection system. CESC consists of four different neural networks for recognizing the classes of attack: DoS,U2R,R2L and Probe. The output data from different neural networks enter the Arbiter for final decision. Extensive experiments on KDDcup99 datasets confirm the superiority of Intrusion detection system based on CESC (IDSCESC) over other recent Intrusion detection system4-14.
incremental kernel PCA Dimensionality reduction expert system committee intrusion detection system
Hou Yong Zheng Xuefeng
School of Information Engineering,University of Science and Technology Beijing,Beijing 100083,China School of Information Engineering,University of Science and Technology Beijing,Beijing 100083,China
国际会议
南宁
英文
21-24
2010-12-10（万方平台首次上网日期，不代表论文的发表时间）