References

Ambwani, T., 2003. Multi class support vector machine implementation to intrusion detection. Proceedings of the International Joint Conference on Neural Networks, Volume 3, July 20-24, 2003, Portland, Oregon, USA., pp: 2300-2305.

Bace, R.G., 2000. Intrusion Detection. Macmillan Technical Publishing, Indianapolis, USA.

Bazan, J., H.S. Nguyen, S.H. Nguyen, P. Synak and J. Wroblewski, 2000. Rough Set lgorithms in Classification Problem. In: Rough Set Methods and Applications, Polkowski, L., S. Tsumoto and T.Y. Lin, (Eds.). Physica-Verlag, Heidelberg, New York, Pp: 49-88.

Bose, I., 2006. Deciding the Financial Health of Dot-Coms Using Rough Sets. School of Business, University of Hong Kong Hong Kong.

Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov., 2: 121-167.
CrossRef  |  Direct Link  |  

Cheng, N., 2015. More than 30 Malaysians fall prey to cyber crime daily. Star Online.

Cortes, C. and V. Vapnik, 1995. Support-vector networks. Mach. Learn., 20: 273-297.
CrossRef  |  Direct Link  |  

De Castro, L.N. and F.J. von Zuben, 2002. Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput., 6: 239-251.
CrossRef  |  

Forrest, S., A.S. Perelson, L. Allen and R. Cherukuri, 1994. Self-nonself discrimination in a computer. Proceedings of the IEEE Computer Society Symposium on Security and Privacy, May 16-18, 1994, Oakland, CA, USA., pp: 202-212.

Golovko, V. and P. Kochurko, 2007. Intrusion Recognition Using Neural Networks. Proceedings of the IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, September 6-8, 2007, Bulgaria, Dortmund, Germany, pp: 108-111.

Gunn, S.R., 1998. Support vector machines for classification and regression. Technical Report, University of Southamptonn.

Hofmann, A., C. Schmitz and B. Sick, 2003. Rule extraction from neural networks for intrusion detection in computer networks systems. Proeedings of the IEEE International Conference on Systems, Man and Cybernetics, October 5-8, 2003, IEEE Inc.,CA., New York Pp: 1259-1265.

Huaping, L., J. Yin and L. Sijia, 2010. A new intelligent intrusion detection method based on attribute reduction and parameters optimization of SVM. Proceedings of the 2nd International Workshop on Education Technology and Computer Science, March 6-7, 2010, Wuhan, China, Pp: 202-205.

Jian, L., G. Zhang and G. Gu, 2004. The research and implementation of intelligent intrusion detection system based on artificial neural network. Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, August 26-29, 2004, Shanghai, China, pp: 3157-3159.

Kendall, K., 1999. A database of computer attacks for the evaluation of intrusion detection systems. Masters Thesis, Massachusetts Institute of Technology, Cambridge, USA

Luyin, C., J. Qingshan and C. Lifei, 2008. A feature selection method for network intrusion detection. Comput. Res. Dev. Suppl., 45: 156-160.

Mukkamala, S., G. Janoski and A. Sung, 2002. Intrusion detection using neural networks and support vector machines. Proceedings of IEEE International Joint Conference on Neural Network, May 12-17, Honolulu, HI, USA., pp: 1702-1707.

Pawlak, Z., 1982. Rough sets. Int. J. Comput. Inform. Sci., 11: 341-356.
CrossRef  |  Direct Link  |  

Shazzad, K.M. and J.S. Park, 2005. Optimization of intrusion detection through fast hybrid feature selection. Proceedings of the 6th International Conference on Parallel and Distributed Computing Applications and Technologies, December 5-8, 2005, Dalian, China, pp: 264-267.

Shi, Y. and R. Eberhart, 1998. A modified particle swarm optimizer. Proceedings of the World Congress on Computational Intelligence and IEEE International Conference on Evolutionary Computation, May 4-9, 1998, Anchorage, AK., pp: 69-73.

Skowron, A. and C. Rauszer, 1992. The Discernibility Matrices and Functions in Information Systems. In: Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Słowinski, R. (Ed.). Kluwer Academic Publisher, Dortrecht, The Netherlands, ISBN: 0792319230, pp: 331.

Starzyk, J.A., D.E. Nelson and K. Sturtz, 2000. A mathematical foundation for improved reduct generation in information systems. Knowl. Inf. Syst., 2: 131-146.
CrossRef  |  Direct Link  |  

Sung, A.H. and S. Mukkamala, 2003. Identify important features for intrusion detection using support vector machines and neural networks. Proceedings of the 2003 Symposium on Application and the Internet, January 27-31, 2003, IEEE Computer Society Washington, DC, USA., pp: 209-216.

Takahashi, F. and S. Abe, 2003. Decision-tree-based multiclass support vector machines. Proceed. Int. Conf. Neural Inf. Proc., 3: 1418-1422.
CrossRef  |  Direct Link  |  

Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York, ISBN-10: 0387945598.

Walczak, B. and D.L. Massart, 1999. Rough sets theory. Chem. Intell. Lab. Syst., 47: 1-16.
CrossRef  |  Direct Link  |  

Wang, T.C. and H.L. Chu, 2005. Applying rough sets theory to bank evaluations of loan applicants credit. Proceedings of the 11th International Conference on Industrial Engineering and Engineering Management, April 23-25, 2005, Northeastern University, Shenyang, China, Pp: 726-730.

Wroblewski, J., 1995. Finding minimal reducts using genetic algorithms. Proceedings of the 2nd Annual Join Conference on Information Sciences, September 28-October 1, 1995, Wrightsville Beach, NC., pp: 186-189.