Communications of the ACM
An introduction to computational learning theory
An introduction to computational learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Practical Intrusion Detection Handbook
Practical Intrusion Detection Handbook
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Precision-recall operating characteristic (P-ROC) curves in imprecise environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Classifier ensembles: Select real-world applications
Information Fusion
A Comparative Study of Data Mining Algorithms for Network Intrusion Detection
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Application of bagging, boosting and stacking to intrusion detection
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A multilayered ensemble architecture for the classification of masses in digital mammograms
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Intrusion detection technology is an effective approach to deal with problems of malicious attacks on computer networks. In this paper, we present an intrusion detection model based on Ensemble of classifiers such as AdaBoost, MultiBoosting and Bagging to gain more opportunity of training misclassified samples and reduce the error rate by the majority voting of involved classifiers. Our main goal is to build an efficient intrusion detection model based on the error rate of classifiers if unfair distribution exists either within or between data sets. We employ data from the third international knowledge discovery and data mining tools competition (KDDCup'99) to train and test feasibility of our proposed model. From our experimental results with KDDCUP'99 benchmarking dataset, the proposed ensemble of classifiers with REP tree as base classifier outperforms others in building a network intrusion detection model with high detection rate, low overall error rate with low false positive rate.