Research Output

A Novel Feature Selection Approach for Intrusion Detection Data Classification

  Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other stateof-the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the previously reported results.

  • Date:

    30 September 2014

  • Publication Status:

    Published

  • Publisher

    Institute of Electrical and Electronics Engineers (IEEE)

  • DOI:

    10.1109/trustcom.2014.15

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

Citation

Ambusaidi, M. A., He, X., Tan, Z., Nanda, P., Lu, L. F., & Nagar, U. T. (2014). A Novel Feature Selection Approach for Intrusion Detection Data Classification. https://doi.org/10.1109/trustcom.2014.15

Authors

Keywords

Feature extraction, Redundancy, Accuracy, Support vector machines, Mutual information, Training, Intrusion detection

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