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A weighted intrusion detection model of dynamic selection

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Abstract

In view of the difficulty of existing intrusion detection methods in dealing with new forms, large scale, and high concealment of network intrusion behaviors, this paper presents a weighted intrusion detection model of the dynamic selection (WIDMoDS) based on data features. The aim is to customize intrusion detection models for network intrusion data sets of different types, sizes and structures. First, according to data features, single classifiers are clustered using a hierarchical clustering algorithm based on the classifiers evaluation indicators, and then, the classifiers selection is by means of accuracy of the single classifiers, in addition, the data-classifier applicable indicators (DCAI) and of the classifiers performances are used for calculating the weights of subjective and objective, and then calculating combined weight ranks. Finally, a custom intrusion detection model is generated by the Weight-voting (W-voting) algorithm. Our experiments show that this model can optimize the number of classifiers based on the data sets features, reduce the problem of redundant or insufficient classifiers in the ensemble process. A new network intrusion detection model of combining the classifier characteristics with the dataset attributes can improve the accuracy of intrusion detection.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61762060), Educational Commission of Gansu Province, China (Grant No.2017C-05), Foundation for the Key Research and Development Program of Gansu Province, China (Grant No.20YF3GA016).The data set comes from the official website. The authors gratefully acknowledge the anonymous reviewers for their helpful comments and suggestions.

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  1. School of Computer and Communications, Lanzhou University of Technology, 287 Lan-gong-ping Road, Lanzhou, Gansu, 730050, China

    Tao Feng & Manfang Dou

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  1. Tao Feng
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  2. Manfang Dou
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Correspondence to Manfang Dou.

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Feng, T., Dou, M. A weighted intrusion detection model of dynamic selection. Appl Intell 51, 4860–4873 (2021). https://doi.org/10.1007/s10489-020-02090-8

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