References

Abadeh, M.S., J. Habibi and C. Lucas, 2007. Intrusion detection using a fuzzy genetics-based learning algorithm. J. Network Comput. Appl., 30: 414-428.
CrossRef  |  

Abadeh, M.S., J. Habibi, Z. Barzegar and M. Sergi, 2007. A parallel genetic local search algorithm for intrusion detection in computer networks. Eng. Appl. Artif. Intell., 20: 1058-1069.
CrossRef  |  

Abe, S. and M.S. Lan, 1995. A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Syst., 3: 18-28.
CrossRef  |  

Ahmed, A.A.E. and L. Traore, 2005. Anomaly intrusion detection based on biometrics. Proceedings of the 6th Annual IEEE SMC Information Assurance Workshop, June 15-17, 2005, West Point, New York, USA., pp: 452-453.

Anderson, D., T.F. Lunt, H. Javitz, A. Tamaru and A. Valdes, 1995. Detecting unusual program behavior using the statistical component of the next-generation intrusion detection expert system (NIDES). Proceeding of the SRI International, Menlo Park, May 1995, Computer Science Laboratory SRI-CSL-95-06, CA -.

Axelsson, S., 2000. Intrusion detection systems: A survey and taxonomy. Technical report no. 99-15. Department of Computer Engineering, Chalmers University of Technology, Sweden.

Cervantes, A., I. Galvan and P. Isasi, 2005. A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm. Proceeding of the 2005 IEEE Congress on Evolutionary Computation, September 5, 2005, Computer Science Department University Carlos III de Madrid, pp: 290-297.

Cho, S. and S. Cha, 2004. SAD: Web session anomaly detection based on parameter estimation. Comput. Security, 23: 312-319.
CrossRef  |  

Cho, S.B., 2002. Incorporating soft computing techniques into a probabilistic intrusion detection system. IEEE Trans. Syst. Man Cybernetics Part C, 32: 154-160.
CrossRef  |  

Cordon, O., F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena, 2004. Ten years of genetic fuzzy systems: Current framework and new trends. Fuzzy Sets Syst., 141: 5-31.
Direct Link  |  

Dasgupta, D. and F. Gonzalez, 2002. An immunity based technique to characterize intrusions in computer networks. IEEE Trans. Evol. Comput., 6: 281-291.
CrossRef  |  

De Falco, I., A.D. Cioppa and E. Tarantino, 2002. Discovering interesting classification rules with genetic programming. Applied Soft Computing, 1: 257-269.
Direct Link  |  

Feng, Y., Z.F. Wu, K.G. Wu, W., Z.Y. Xiong and Y. Zhou, 2005. An unsupervised anomaly intrusion detection algorithm based on swarm intelligence. Proceedings of the fourth international conference on machine learning and cybernetics, August 18-21, 2005, Guangzhou, pp: 3965-3969.

Gao, H.H., H.H. Yang and X.Y. Wang, 2005. Ant colony optimization based network intrusion feature selection and detection. Proc. Int. Conf. Machine Learn. Cybernetics, 6: 3871-3875.
CrossRef  |  

Guan, Y., A.A. Ghorbani and N. Belacel, 2003. Y-MEANS: A clustering method for intrusion detection. Proc. Can. Conf. Electr. Comput. Eng., 2: 1083-1086.
CrossRef  |  

Harmer, P.K., P.D. Williams, G.H. Gunsch and G.B. Lamont, 2002. An artificial immune system architecture for computer security applications. IEEE Trans. Evol. Comput., 6: 252-280.
Direct Link  |  

Heady, R., G. Luger, A. Maccabe and M. Servilla, 1990. The architecture of network level intrusion detection system. Technical Report, Department of Computer Science, University of New Mexico.

Hofmann, F., 2004. Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets Syst., 141: 47-58.
CrossRef  |  

Hu, Y.C., R.S. Chen and G.H. Tzeng, 2003. Finding fuzzy classification rules using data mining techniques. Pattern Recognit. Lett., 24: 509-519.
CrossRef  |  

Idris, N.B. and B. Shanmugam, 2005. Artificial intelligence techniques applied to intrusion detection. Proceedings of Annual IEEE Indicon, December 11-13, 2005, Institute of Electrical and Electronics Engineers, pp:52-55.

Ilgun, K., 1993. USTAT: A real-time intrusion detection system for UNIX. Proceedings of the 1993 Computer Society Symposium on Research in Security and Privacy, Oakland, May 24-26, 1993, IEEE Computer Society Press, Los Alamitos, pp: 16-28.

Ishibuchi, H. and T. Yamamoto, 2004. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst., 141: 59-88.
CrossRef  |  

Ishibuchi, H., K. Nozaki and H. Tanaka, 1992. Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst., 52: 21-32.
CrossRef  |  

Ishibuchi, H., T. Nakashima and T. Murata, 1999. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. B Cybern., 19: 601-618.
CrossRef  |  PubMed  |  

Ishibuchi, H., T. Nakashima and T. Murata, 2001. Three-objective genetics-based machine learning for linguistic rule extraction. Info. Sci., 136: 109-133.
CrossRef  |  Direct Link  |  

Ishibuchi, H., T. Yamamoto and T. Nakashima, 2005. Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans. Syst. Man Cybern. B Cybern., 35: 359-365.
CrossRef  |  

Kruegel, C. and G. Vigna, 2003. Anomaly detection of web-based attacks. Proceedings of 10th ACM Conference on Computer and Communications Security, October 27-30, 2003, ACM Press, New York, USA., pp: 251-261.

Lee, C.C., 1990. Fuzzy logic in control systems: Fuzzy logic controller. II. IEEE Trans. Syst. Man Cybernet., 20: 419-435.
CrossRef  |  Direct Link  |  

Lee, S.C. and D.V. Heinbuch, 2001. Training a neural-network based intrusion detector to recognize novel attacks. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum., 31: 294-299.
CrossRef  |  

Lee, W., S.J. Stolfo and K.W. Mok, 1998. Mining audit data to build intrusion detection models. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, August 27-31, 1998, AAAI Press, New York, pp: 1-20.

Leon, E., O. Nasraoui and J. Gomez, 2004. Anomaly detection based on unsupervised niche clustering with application to network intrusion detection. IEEE Conf. Evol. Comput., 1: 502-508.
CrossRef  |  

Lunt, T., A. Tamaru, F. Gilham, R. Jagannathan and C. Jalali et al., 1992. A real time intrusion detection expert system (IDES). Final Report.SRI Interna-tional, Menlo Park, CA.

Mitra, S. and S.K. Pal, 1994. Self-organizing neural network as a fuzzy classifier. IEEE Trans. Syst. Man Cybernet., 24: 385-399.
CrossRef  |  

Murali, A. and M. Rao, 2005. A Survey on intrusion detection approaches. Proceedings of the 1st International Conference on Information and Communication Technologies, August 27-28, 2005, Computer Centre University of Hyderabad India, pp: 233-240.

Oh, S.H. and W.S. Lee, 2003. An anomaly intrusion detection method by clustering normal user behavior. Comput. Secur., 22: 596-612.
Direct Link  |  

Ozyer, T., R. Alhajj and K. Barker, 2007. Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening. J. Network Comput. Appl., 30: 99-113.
CrossRef  |  

Provost, F., T. Fawcett and R. Kohavi, 1998. The case against accuracy estimation for comparing induction algorithms. Proceedings of the 15th International Conference on Machine Learning, July 24-27, 1998, Morgan Kaufmann, pp: 445-453.

Rouwhorst, S.E. and A.P. Engelbrecht, 2000. Searching the forest: Using decision trees as building blocks for evolutionary search in classification databases. IEEE Congr. Evol. Comput., 1: 633-638.
CrossRef  |  

Sugeno, M., 1985. An introductory survey of fuzzy control. Inform. Sci., 36: 59-83.
Direct Link  |  

Tan, K.C., Q. Yu, C.M. Heng and T.H. Lee, 2003. Evolutionary computing for knowledge discovery in medical diagnosis. Artif. Intell. Med., 27: 129-154.
PubMed  |  

Tian, J.F., X. Ying, F. Yue and J.L. Wang, 2005. Intrusion detection combining multiple decision trees by fuzzy logic. Proceedings of Sixth International Conference on Parallel and Distributed Computing Applications and Technologies, December 5-8, 2005, IEEE Computer Security, pp: 256-258.

Wang, L.X. and J.M. Mendel, 1992. Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern., 22: 1414-1427.
CrossRef  |  

Xu, B. and A. Zhang, 2005. Application of support vector clustering algorithm to network intrusion detection. Proceedings of International Conference on Neural Networks and Brain, October 13-15, 2005, IEEE Computer Security, USA., pp: 1036-1040.

Yang, X.R., J.Y. Shen and R. Wang, 2002. Artificial immune theory based network intrusion detection system and the algorithms design. Int. Conf. Mach. Learn. Cybern. Beijing, 1: 73-77.
CrossRef  |  

Ye, N., S. Vilbert and Q. Chen, 2003. Computer intrusion detection through EWMA for autocorrelated and uncorrelated data. IEEE Trans. Reliab., 52: 75-82.
CrossRef  |