Feature selection and intrusion detection using hybrid flexible neural tree

Lecture Notes in Computer Science

Volumn 3498, Issue III, 2005, Pages 439-444

  Chen, Yuehui ^a   Abraham, Ajith ^b   Yang, Ju ^a  

^a UNIVERSITY OF JINAN   (China)

^b Chung Ang University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; FEATURE EXTRACTION; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; OPTIMIZATION;

FEATURE SELECTION; FLEXIBLE NEURAL TREE (FNT); INTRUSION DETECTION SYSTEMS (IDS); PARTICLE SWARM OPTIMIZATION;

NEURAL NETWORKS;

EID: 24944552355     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11427469_71     Document Type: Conference Paper

References (9)

1. Debar, M., Becke, D., Siboni, A.: A Neural Network Component for an Intrusion Detection System. Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy (1992)
2. Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion Detection Using Ensemble of Soft Computing Paradigms. Advances in Soft Computing, Springer Verlag, Germany (2003) 239-248
3. Shah, K., Dave, N., Chavan, S., Mukherjee, S., Abraham, A., Sanyal, S.: Adaptive NeuroFuzzy Intrusion Detection System. IEEE International Conference on ITCC'04, 1 (2004) 70-74
4. Abraham, A.: Evolutionary Computation in Intelligent Web Management. Evolutionary Computing in Data Mining, A. Ghosh and L. Jain (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, Chapter 8 (2004) 189-210
5. Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series Forecasting using Flexible Neural Tree Model. Information Science, (2005) In press
6. KDD cup 99, http://kdd.ics.uci.edu/database/kddcup99/kddcup.data_10_percent.gz
7. Chebrolu, S., Abraham, A., Thomas, J.P. : Feature Detection and Ensemble Design of Intrusion Detection Systems. Computers and security, (http://dx.doi.org/10.1016/j.cose.2004.09.008), In press.
8. Barbara, D., Couto, J., Jajodia, S., Wu, N.: ADAM: A Testbed for Exploring the Use of Data Mining in Intrusion Detection. SIGMOD Record, 30 (2001) 15-24
9. Joo, D., Hong, T., Han, T.: The Neural Network Models for IDS Based on the Asymmetric Costs of False Negative Errors and False Positive Errors. Expert Systems with Applications, 25 (2003) 69-75