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AdaBoost-based algorithm for network intrusion detection.
IEEE Trans Syst Man Cybern B Cybern. 2008 Apr; 38(2):577-83.IT

Abstract

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.

Authors+Show Affiliations

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. wmhu@nlpr.ia.ac.cnNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18348941

Citation

Hu, Weiming, et al. "AdaBoost-based Algorithm for Network Intrusion Detection." IEEE Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society, vol. 38, no. 2, 2008, pp. 577-83.
Hu W, Hu W, Maybank S. AdaBoost-based algorithm for network intrusion detection. IEEE Trans Syst Man Cybern B Cybern. 2008;38(2):577-83.
Hu, W., Hu, W., & Maybank, S. (2008). AdaBoost-based algorithm for network intrusion detection. IEEE Transactions On Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society, 38(2), 577-83. https://doi.org/10.1109/TSMCB.2007.914695
Hu W, Hu W, Maybank S. AdaBoost-based Algorithm for Network Intrusion Detection. IEEE Trans Syst Man Cybern B Cybern. 2008;38(2):577-83. PubMed PMID: 18348941.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - AdaBoost-based algorithm for network intrusion detection. AU - Hu,Weiming, AU - Hu,Wei, AU - Maybank,Steve, PY - 2008/3/20/pubmed PY - 2008/5/7/medline PY - 2008/3/20/entrez SP - 577 EP - 83 JF - IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society JO - IEEE Trans Syst Man Cybern B Cybern VL - 38 IS - 2 N2 - 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. SN - 1083-4419 UR - https://www.unboundmedicine.com/medline/citation/18348941/AdaBoost_based_algorithm_for_network_intrusion_detection_ L2 - https://dx.doi.org/10.1109/TSMCB.2007.914695 DB - PRIME DP - Unbound Medicine ER -