Research Output

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

  Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

  • Type:

    Article

  • Date:

    19 January 2016

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:

    10.1109/tc.2016.2519914

  • ISSN:

    0018-9340

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004.2 Systems analysis, design & performance

Citation

Ambusaidi, M. A., He, X., Nanda, P., & Tan, Z. (2016). Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm. IEEE Transactions on Computers, 65(10), 2986-2998. https://doi.org/10.1109/tc.2016.2519914

Authors

Keywords

Theoretical Computer Science; Hardware and Architecture; Computational Theory and Mathematics; Software

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