Decision trees and qualitative possibilistic inference: Application to the intrusion detection problem

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2711, Issue , 2003, Pages 419-431

  Amor, Nahla Ben ^a   Benferhat, Salem ^b   Elouedi, Zied ^a   Mellouli, Khaled ^a  

^a UNIVERSITY OF TUNIS   (Tunisia)

^b UNIVERSITÉ D'ARTOIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION THEORY; LOGIC PROGRAMMING; PROBLEM SOLVING; SET THEORY; DECISION TREES; FORESTRY; MERCURY (METAL);

DECISION TREES; INTRUSION DETECTION PROBLEM; QUALITATIVE POSSIBILISTIC INFERENCE; ABNORMAL BEHAVIOURS; CLASSIFICATION PROCEDURE; INTRUSION DETECTION SYSTEMS; POSSIBILISTIC; POSSIBILISTIC LOGIC; POSSIBILITY DISTRIBUTIONS; UNCERTAIN OBSERVATIONS;

TREES (MATHEMATICS); INTRUSION DETECTION;

EID: 8344223726     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45062-7_34     Document Type: Conference Paper

References (16)

1. Axelsson, S.: Intrusion detection systems: a survey and taxonomy, Technical report 99-15, March 2000.
2. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees, Monterey, CA: Wadsworth & Brooks, 1984.
3. Denoeux T., Skarstein- Bjanger M.: Induction of decision trees for partially classified data, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Nashville, USA, 2923-2928, 2000.
4. Dubois, D., Lang, J., Prade, H.: Possibilistic logic. In Handbook on Logic in Artificial Intelligence and Logic Programming, 3, 439-513, 1994.
5. Elouedi Z., Mellouli K., Smets P.: Belief decision trees: Theoretical foundations, International Journal of Approximate Reasoning 28, 91-124, 2001.
6. Hullermeier E., Possibilistic induction in decision-tree learning, ECML'02, 2002.
7. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning of rules and trees. In machine Learning. Neural and statistical Classification. Ellis Horwood, 1994.
8. Porras, P.A., Neumann., P.G.: EMERALD: Event monitoring enabling responses to anomalous live disturbances. In Proceedings of the 20th National Information Systems Security Conference, 353-365, 1997.
9. http://kdd.ccs.uci.edu/databases/kddcup99/task.html.
10. Portier, P., Froment-Curtil J.: Data mining techniques for intrusion detection, Technical report, University of Texas at Austin, Spring, 2000.
11. Quinlan, J.R.: Induction of decision trees, Machine Learning 1, 1-106, 1986.
12. Quinlan, J.R.: Probabilistic decision trees, Machine Learning, Vol. 3, Chap. 5, Morgan Kaufmann, 267-301, 1990.
13. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann San Mateo Ca, 1993.
14. Mingers, J.: An empirical comparison of selection measures for decision tree induction. Machine learning, 4, 227-243, 1989.
15. Mitchell, T.M.: Decision tree learning. Chapter 3 of Machine Learning, Co-published by the MIT Press and the McGraw-Hill Compagnies, Inc., 1997.
16. Fayyad, U.M., Irani, K.B.: On the handling of continuous-valuues attributes in decision tree generation. Machine Learning, 8, 87-102, 1992