Hybrid feature selection for modeling intrusion detection systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3316, Issue , 2004, Pages 1020-1025

  Chebrolu, Srilatha ^a   Abraham, Ajith ^a   Thomas, Johnson P ^a  

^a OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; COMPUTER CRIME; INTRUSION DETECTION; MERCURY (METAL);

CLASSIFICATION AND REGRESSION TREE; DETECTION PROCESS; DETECTION SYSTEM; FEATURE SELECTION ALGORITHM; HYBRID ARCHITECTURES; HYBRID FEATURE SELECTIONS; INTRUSION DETECTION SYSTEMS; MISUSE PATTERNS;

FEATURE EXTRACTION;

EID: 33751119481     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30499-9_158     Document Type: Article

References (9)

1. Brieman L., Friedman J., Olshen R. and Stone C., Classification of Regression Trees. Wadsworth Inc., 1984.
2. Cheng J., Greiner R., Kelly J., Bell D.A. and Liu W., Learning Bayesian Networks from Data: an Information-Theory Based Approach, The Artificial Intelligence Journal, Volume 137, Pages 43-90, 2002.
3. KDD cup 99 Intrusion detection data set
4. Lee W., Stolfo S. and Mok K., A Data Mining Framework for Building Intrusion Detection Models, In Proceedings of the IEEE Symposium on Security and Privacy, 1999.
5. Luo J. and Bridges S. M., Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection, International Journal of Intelligent Systems, John Wiley & Sons, Vol. 15, No. 8, pp. 687-704, 2000.
6. MIT Lincoln Laboratory.
7. Mukkamala S., Sung A.H. and Abraham A., Intrusion Detection Using Ensemble of Soft Computing Paradigms, Third International Conference on Intelligent Systems Design and Applications, Springer Verlag Germany, pp. 239-248, 2003.
8. Sung A.H. and Mukkamala S., Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks, Proceedings of International Symposium on Applications and the Internet (SAINT 2003), pp. 209-217, 2003.
9. Tsamardinos I., Aliferis C.F.and Statnikov A., Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations, 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, USA, ACM Press, pages 673-678, 2003.