A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
A modular multiple classifier system for the detection of intrusions in computer networks
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
An adaptive automatically tuning intrusion detection system
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Fuzzy model tuning for intrusion detection systems
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
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Varied data mining techniques have been developed for intrusion detection. However, it is unclear which data mining technique is most effective. In this paper, we present our research work in developing a Multi-Class SLIPPER (MC-SLIPPER) system for intrusion detection to learn whether we can get benefit from boosting based learning algorithm. The key idea is to use the available binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. Multiple arbitral strategies based on prediction confidence are proposed to arbitrate results from all binary SLIPPER modules. Our system is evaluated on the KDDCUPý99 intrusion detection dataset. The experimental results show that we get best performance using a 5-7-5 BP neural network; and the performance using other arbitral strategies are better than the winner of the contest does in term of misclassification cost (MC).