Combining incremental Hidden Markov Model and Adaboost algorithm for anomaly intrusion detection
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Detection of Database Intrusion Using a Two-Stage Fuzzy System
ISC '09 Proceedings of the 12th International Conference on Information Security
Adaptive Distributed Intrusion Detection Using Parametric Model
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new intrusion detection method based on antibody concentration
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Probabilistic self-organizing maps for qualitative data
Neural Networks
Journal of Real-Time Image Processing
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
Decision tree based light weight intrusion detection using a wrapper approach
Expert Systems with Applications: An International Journal
A noise-detection based AdaBoost algorithm for mislabeled data
Pattern Recognition
Anomaly based intrusion detection using meta ensemble classifier
Proceedings of the Fifth International Conference on Security of Information and Networks
Minimal complexity attack classification intrusion detection system
Applied Soft Computing
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Two-stage database intrusion detection by combining multiple evidence and belief update
Information Systems Frontiers
Infinite Dirichlet mixture models learning via expectation propagation
Advances in Data Analysis and Classification
Future Generation Computer Systems
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Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.