IEEE Transactions on Software Engineering - Special issue on computer security and privacy
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
NetSTAT: a network-based intrusion detection system
Journal of Computer Security
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
An Application of Machine Learning to Network Intrusion Detection
ACSAC '99 Proceedings of the 15th Annual Computer Security Applications Conference
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Online Training of SVMs for Real-time Intrusion Detection
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Two-tier based intrusion detection system
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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With the rapid increase in connectivity and accessibility of computer systems over the internet which has resulted in frequent opportunities for intrusions and attacks, intrusion detection on the network has become a crucial issue for computer system security. Methods based on hand-coded rule sets are laborous to build and not very reliable. This problem has led to an increasing interest in intrusion detection techniques based upon machine learning or data mining. However, traditional data mining based intrusion detection systems use single classifier in their detection engines. In this paper, we propose a meta learning based method for intrusion detection by MultiBoosting multi classifiers. MultiBoosting can form decision committees by combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Experiments results show that MultiBoosting can improve the detection performance of state-of-art machine learning based intrusion detection techniques. Furthermore, we present a Symmetrical Uncertainty (SU) based method for reducing network connection features to make MultiBoosting more efficient in real-time network environment, in the meanwhile, keep the detection performance unundermined and in some cases, even further improved.