A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection
| LEADER | caa a22 4500 | ||
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| 001 | 605470405 | ||
| 003 | CHVBK | ||
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| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150501xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1332-7 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1332-7 | ||
| 245 | 0 | 2 | |a A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection |h [Elektronische Daten] |c [Fangjun Kuang, Siyang Zhang, Zhong Jin, Weihong Xu] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Intrusion detection |2 nationallicence | |
| 690 | 7 | |a Kernel principal component analysis |2 nationallicence | |
| 690 | 7 | |a Support vector machine |2 nationallicence | |
| 690 | 7 | |a Chaotic particle swarm optimization |2 nationallicence | |
| 700 | 1 | |a Kuang |D Fangjun |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | |
| 700 | 1 | |a Zhang |D Siyang |u Department of Electrical and Information Engineering, Hunan Vocational Institute of Safety and Technology, 410151, Changsha, China |4 aut | |
| 700 | 1 | |a Jin |D Zhong |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | |
| 700 | 1 | |a Xu |D Weihong |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/5(2015-05-01), 1187-1199 |x 1432-7643 |q 19:5<1187 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1332-7 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1332-7 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kuang |D Fangjun |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhang |D Siyang |u Department of Electrical and Information Engineering, Hunan Vocational Institute of Safety and Technology, 410151, Changsha, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Jin |D Zhong |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Xu |D Weihong |u School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/5(2015-05-01), 1187-1199 |x 1432-7643 |q 19:5<1187 |1 2015 |2 19 |o 500 | ||