caa a22 4500 605470405 CHVBK 20210128100327.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1332-7 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1332-7 A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection [Elektronische Daten] [Fangjun Kuang, Siyang Zhang, Zhong Jin, Weihong Xu] A novel support vector machine (SVM) model by combining kernel principal component analysis (KPCA) with improved chaotic particle swarm optimization (ICPSO) is proposed to deal with intrusion detection. The proposed method, in which multi-layer SVM classifier is employed to estimate whether the action is an attack, KPCA is applied as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. To shorten the training time and improve the performance of SVM, N-RBF is employed to reduce the noise generated by feature differences, and ICPSO is presented to optimize the punishment factor C, kernel parameters $$\sigma $$ σ and the tube size $$\varepsilon $$ ε of SVM, which introduces chaos optimization and premature processing mechanism. Experimental results illustrate that the improved SVM model has faster computational time and higher predictive accuracy, and it can also shorten the training time and improve the performance of SVM. Springer-Verlag Berlin Heidelberg, 2014 Intrusion detection nationallicence Kernel principal component analysis nationallicence Support vector machine nationallicence Chaotic particle swarm optimization nationallicence Kuang Fangjun School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut Zhang Siyang Department of Electrical and Information Engineering, Hunan Vocational Institute of Safety and Technology, 410151, Changsha, China aut Jin Zhong School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut Xu Weihong School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1187-1199 1432-7643 19:5<1187 2015 19 500 https://doi.org/10.1007/s00500-014-1332-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1332-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kuang Fangjun School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut NATIONALLICENCE 700 1- Zhang Siyang Department of Electrical and Information Engineering, Hunan Vocational Institute of Safety and Technology, 410151, Changsha, China aut NATIONALLICENCE 700 1- Jin Zhong School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut NATIONALLICENCE 700 1- Xu Weihong School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1187-1199 1432-7643 19:5<1187 2015 19 500