An intrusion detection model based on GS-SVM classifier

Information Technology Journal

Volumn 11, Issue 7, 2012, Pages 794-798

  Lei, Xiangyu ^a   Zhou, Ping ^a  

^a GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

Author keywords

Cross Validation; Grid Search; Intrusive behavior; Kernel function; Support Vector Machine

Indexed keywords

CROSS VALIDATION; DATA PREPROCESSING; DATA SETS; DETECTION RATES; GRID SEARCH; INTRUSION DETECTION MODELS; INTRUSION DETECTION SYSTEMS; INTRUSIVE BEHAVIOR; KERNEL FUNCTION; MEMORY RESOURCES; ONE-CLASS SUPPORT VECTOR MACHINE; OPTIMIZATION ALGORITHMS; PARAMETER SELECTION; TRAINING TIME;

ALGORITHMS; INTRUSION DETECTION;

SUPPORT VECTOR MACHINES;

EID: 84863200267     PISSN: 18125638     EISSN: 18125646     Source Type: Journal    
DOI: 10.3923/itj.2012.794.798     Document Type: Article

References (16)

1. Al-Tameemi, A. M., L. Zheng and M. Khalifa, 2011. Off-line arabic words classification using multi-set features. Inf. Technol. J., 10:1754-1760.
2. Chen, Z. G. and S. Wang, 2008. Minimax probability machine with genetic feature optimized for intrusion detection. Inform. Technol. J., 7:185-189. (Pubitemid 351461970)
3. El-Kouatly, R. and G. A. Salman, 2008. A radial basic function with multiple input and multiple output neural network to control a non-linear plant of unknown dynamics. Inform. Technol. J., 7:430-439.
4. Gu, Y., B. Zhou and J. Zhao, 2008. PCA-ICA ensembled intrusion detection system by pareto-optimal optimization. Inform. Technol. J., 7:510-515.
5. Hsu, C. W. and C. J. Lin, 2002. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, 13:415-425.
6. Keerthi, S. S., S. K. Shevede, C. Bhattacharyya and K. R. K. Murthy, 2000. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Networks, 11:124-136.
7. Liejun, W., J. Zhenhong and L. Zhaogan, 2008. Multi-resolution signal decomposition and approximation based on SVMS. Inform. Technol. J., 7:320-325.
8. Lijia, C, Z. Shengxiu, L. Xiaofeng, L. Yinan and L. Ying, 2011. Nonlinear adaptive block backstepping control using command filter and neural networks approximation. Infor. Technol. J., 10:2284-2291.
9. Mabu, Y. G., S. C. Chen, Y. Wang and K. Hirasawa, 2009. Intrusion detection system combining misuse detection and anomaly detection using Genetic Network Programming. Proceedings of the ICCAS-SICE, August 18-21, 2009, Waseda University, Kitakyushu, Japan, pp: 3463-3467.
10. Macia-Perez, F., F. Mora-Gimeno, D. Marcos-Jorquera, J. A. Gil-Martinez-Abarca, H. Ramos-Morillo and I. Lorenzo-Fonseca, 2011. Network intrusion detection system embedded on a smart sensor. IEEE Trans. Ind. Electron., 58:722-732.
11. Muda, Z., W. Yassin, M. N. Sulaiman and N. I. Udzir, 2011. A K-means and naive bayes learning approach for better intrusion detection. Inform. Technol. J., 10:648-655.
12. Rehak, M., M. Pechoucek, M. Grill, J. Stiborek, K. Bartos and P. Celeda, 2009. Adaptive multiagent system for network traffic monitoring. Intell. Sys., 24:16-25.
13. Shuchun, Y., Y. Xiaoyang, S. lina, Z. Yuping and S. Yongbin et al., 2011. A reconstruction method for disparity image based on region segmentation and RBF neural network. Inform. Technol. J., 10:1050-1055.
14. Stolfo, S. J., W. Lee, P. K. Chan, W. Fan and E. Eskin, 2001. Data mining-based intrusion detectors: An overview of the Columbia IDS project. ACM Sigmod Rec, 30:5-14.
15. Yang, M. N., X. J. LI and X. H. Zhang, 2011. Robust description method of SIFT for features of license plate characters. Inf. Technol. J., 10:2189-2195.
16. Zaina, A., M. A. Maarof and S. M. Shamsuddin, 2006. Feature selection using rough set in intrusion detection. Proceedings of the TENCON 2006 IEEE Region of 10 Conference, November 14-17, 2006, Teknologi Malaysia, Johor, pp: 1-4.