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    AdaBoost-based algorithm for network intrusion detection

    Hu, W. and Hu, W. and Maybank, Stephen J. (2008) AdaBoost-based algorithm for network intrusion detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38 (2), pp. 577-583. ISSN 1083-4419.

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    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.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 02 Feb 2011 12:42
    Last Modified: 17 Apr 2013 12:18
    URI: http://eprints.bbk.ac.uk/id/eprint/1884

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